Merge pull request #3273 from LDOUBLEV/distill

Add det distill

configs/det/ch_ppocr_v2.1/ch_det_lite_train_cml_v2.1.yml

+Global:
+  use_gpu: true
+  epoch_num: 1200
+  log_smooth_window: 20
+  print_batch_step: 2
+  save_model_dir: ./output/ch_db_mv3/
+  save_epoch_step: 1200
+  # evaluation is run every 5000 iterations after the 4000th iteration
+  eval_batch_step: [3000, 2000]
+  cal_metric_during_train: False
+  pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
+  checkpoints:
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: doc/imgs_en/img_10.jpg
+  save_res_path: ./output/det_db/predicts_db.txt
+
+Architecture:
+  name: DistillationModel
+  algorithm: Distillation
+  Models:
+    Student:
+      pretrained: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
+      freeze_params: false
+      return_all_feats: false
+      model_type: det
+      algorithm: DB
+      Backbone:
+        name: MobileNetV3
+        scale: 0.5
+        model_name: large
+        disable_se: True
+      Neck:
+        name: DBFPN
+        out_channels: 96
+      Head:
+        name: DBHead
+        k: 50
+    Student2:
+      pretrained: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
+      freeze_params: false
+      return_all_feats: false
+      model_type: det
+      algorithm: DB
+      Transform:
+      Backbone:
+        name: MobileNetV3
+        scale: 0.5
+        model_name: large
+        disable_se: True
+      Neck:
+        name: DBFPN
+        out_channels: 96
+      Head:
+        name: DBHead
+        k: 50
+    Teacher:
+      pretrained: ./pretrain_models/ch_ppocr_server_v2.0_det_train/best_accuracy
+      freeze_params: true
+      return_all_feats: false
+      model_type: det
+      algorithm: DB
+      Transform:
+      Backbone:
+        name: ResNet
+        layers: 18
+      Neck:
+        name: DBFPN
+        out_channels: 256
+      Head:
+        name: DBHead
+        k: 50
+
+Loss:
+  name: CombinedLoss
+  loss_config_list:
+  - DistillationDilaDBLoss:
+      weight: 1.0
+      model_name_pairs:
+      - ["Student", "Teacher"]
+      - ["Student2", "Teacher"]
+      key: maps
+      balance_loss: true
+      main_loss_type: DiceLoss
+      alpha: 5
+      beta: 10
+      ohem_ratio: 3
+  - DistillationDMLLoss:
+      model_name_pairs:
+      - ["Student", "Student2"]
+      maps_name: "thrink_maps"
+      weight: 1.0
+      # act: None
+      model_name_pairs: ["Student", "Student2"]
+      key: maps
+  - DistillationDBLoss:
+      weight: 1.0
+      model_name_list: ["Student", "Student2"]
+      # key: maps
+      # name: DBLoss
+      balance_loss: true
+      main_loss_type: DiceLoss
+      alpha: 5
+      beta: 10
+      ohem_ratio: 3
+
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+    warmup_epoch: 2
+  regularizer:
+    name: 'L2'
+    factor: 0
+
+PostProcess:
+  name: DistillationDBPostProcess
+  model_name: ["Student", "Student2", "Teacher"]
+  # key: maps
+  thresh: 0.3
+  box_thresh: 0.6
+  max_candidates: 1000
+  unclip_ratio: 1.5
+
+Metric:
+  name: DistillationMetric
+  base_metric_name: DetMetric
+  main_indicator: hmean
+  key: "Student"
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/icdar2015/text_localization/
+    label_file_list:
+      - ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
+    ratio_list: [1.0]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - DetLabelEncode: # Class handling label
+      - IaaAugment:
+          augmenter_args:
+            - { 'type': Fliplr, 'args': { 'p': 0.5 } }
+            - { 'type': Affine, 'args': { 'rotate': [-10, 10] } }
+            - { 'type': Resize, 'args': { 'size': [0.5, 3] } }
+      - EastRandomCropData:
+          size: [960, 960]
+          max_tries: 50
+          keep_ratio: true
+      - MakeBorderMap:
+          shrink_ratio: 0.4
+          thresh_min: 0.3
+          thresh_max: 0.7
+      - MakeShrinkMap:
+          shrink_ratio: 0.4
+          min_text_size: 8
+      - NormalizeImage:
+          scale: 1./255.
+          mean: [0.485, 0.456, 0.406]
+          std: [0.229, 0.224, 0.225]
+          order: 'hwc'
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] # the order of the dataloader list
+  loader:
+    shuffle: True
+    drop_last: False
+    batch_size_per_card: 8
+    num_workers: 4
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/icdar2015/text_localization/
+    label_file_list:
+      - ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - DetLabelEncode: # Class handling label
+      - DetResizeForTest:
+#           image_shape: [736, 1280]
+      - NormalizeImage:
+          scale: 1./255.
+          mean: [0.485, 0.456, 0.406]
+          std: [0.229, 0.224, 0.225]
+          order: 'hwc'
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 1 # must be 1
+    num_workers: 2

configs/det/ch_ppocr_v2.1/ch_det_lite_train_distill_v2.1.yml

+Global:
+  use_gpu: true
+  epoch_num: 1200
+  log_smooth_window: 20
+  print_batch_step: 2
+  save_model_dir: ./output/ch_db_mv3/
+  save_epoch_step: 1200
+  # evaluation is run every 5000 iterations after the 4000th iteration
+  eval_batch_step: [3000, 2000]
+  cal_metric_during_train: False
+  pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
+  checkpoints:
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: doc/imgs_en/img_10.jpg
+  save_res_path: ./output/det_db/predicts_db.txt
+
+Architecture:
+  name: DistillationModel
+  algorithm: Distillation
+  Models:
+    Student:
+      pretrained: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
+      freeze_params: false
+      return_all_feats: false
+      model_type: det
+      algorithm: DB
+      Backbone:
+        name: MobileNetV3
+        scale: 0.5
+        model_name: large
+        disable_se: True
+      Neck:
+        name: DBFPN
+        out_channels: 96
+      Head:
+        name: DBHead
+        k: 50
+    Teacher:
+      pretrained: ./pretrain_models/ch_ppocr_server_v2.0_det_train/best_accuracy
+      freeze_params: true
+      return_all_feats: false
+      model_type: det
+      algorithm: DB
+      Transform:
+      Backbone:
+        name: ResNet
+        layers: 18
+      Neck:
+        name: DBFPN
+        out_channels: 256
+      Head:
+        name: DBHead
+        k: 50
+
+Loss:
+  name: CombinedLoss
+  loss_config_list:
+  - DistillationDilaDBLoss:
+      weight: 1.0
+      model_name_pairs:
+      - ["Student", "Teacher"]
+      key: maps
+      balance_loss: true
+      main_loss_type: DiceLoss
+      alpha: 5
+      beta: 10
+      ohem_ratio: 3
+  - DistillationDBLoss:
+      weight: 1.0
+      model_name_list: ["Student", "Teacher"]
+      # key: maps
+      name: DBLoss
+      balance_loss: true
+      main_loss_type: DiceLoss
+      alpha: 5
+      beta: 10
+      ohem_ratio: 3
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+    warmup_epoch: 2
+  regularizer:
+    name: 'L2'
+    factor: 0
+
+PostProcess:
+  name: DistillationDBPostProcess
+  model_name: ["Student", "Student2"]
+  key: head_out
+  thresh: 0.3
+  box_thresh: 0.6
+  max_candidates: 1000
+  unclip_ratio: 1.5
+
+Metric:
+  name: DistillationMetric
+  base_metric_name: DetMetric
+  main_indicator: hmean
+  key: "Student"
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/icdar2015/text_localization/
+    label_file_list:
+      - ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
+    ratio_list: [1.0]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - DetLabelEncode: # Class handling label
+      - IaaAugment:
+          augmenter_args:
+            - { 'type': Fliplr, 'args': { 'p': 0.5 } }
+            - { 'type': Affine, 'args': { 'rotate': [-10, 10] } }
+            - { 'type': Resize, 'args': { 'size': [0.5, 3] } }
+      - EastRandomCropData:
+          size: [960, 960]
+          max_tries: 50
+          keep_ratio: true
+      - MakeBorderMap:
+          shrink_ratio: 0.4
+          thresh_min: 0.3
+          thresh_max: 0.7
+      - MakeShrinkMap:
+          shrink_ratio: 0.4
+          min_text_size: 8
+      - NormalizeImage:
+          scale: 1./255.
+          mean: [0.485, 0.456, 0.406]
+          std: [0.229, 0.224, 0.225]
+          order: 'hwc'
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] # the order of the dataloader list
+  loader:
+    shuffle: True
+    drop_last: False
+    batch_size_per_card: 8
+    num_workers: 4
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/icdar2015/text_localization/
+    label_file_list:
+      - ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - DetLabelEncode: # Class handling label
+      - DetResizeForTest:
+#           image_shape: [736, 1280]
+      - NormalizeImage:
+          scale: 1./255.
+          mean: [0.485, 0.456, 0.406]
+          std: [0.229, 0.224, 0.225]
+          order: 'hwc'
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 1 # must be 1
+    num_workers: 2

configs/det/ch_ppocr_v2.1/ch_det_lite_train_dml_v2.1.yml

+Global:
+  use_gpu: true
+  epoch_num: 1200
+  log_smooth_window: 20
+  print_batch_step: 2
+  save_model_dir: ./output/ch_db_mv3/
+  save_epoch_step: 1200
+  # evaluation is run every 5000 iterations after the 4000th iteration
+  eval_batch_step: [3000, 2000]
+  cal_metric_during_train: False
+  pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
+  checkpoints:
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: doc/imgs_en/img_10.jpg
+  save_res_path: ./output/det_db/predicts_db.txt
+
+Architecture:
+  name: DistillationModel
+  algorithm: Distillation
+  Models:
+    Student:
+      pretrained: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
+      freeze_params: false
+      return_all_feats: false
+      model_type: det
+      algorithm: DB
+      Backbone:
+        name: MobileNetV3
+        scale: 0.5
+        model_name: large
+        disable_se: True
+      Neck:
+        name: DBFPN
+        out_channels: 96
+      Head:
+        name: DBHead
+        k: 50
+    Student2:
+      pretrained: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
+      freeze_params: false
+      return_all_feats: false
+      model_type: det
+      algorithm: DB
+      Transform:
+      Backbone:
+        name: MobileNetV3
+        scale: 0.5
+        model_name: large
+        disable_se: True
+      Neck:
+        name: DBFPN
+        out_channels: 96
+      Head:
+        name: DBHead
+        k: 50
+
+
+Loss:
+  name: CombinedLoss
+  loss_config_list:
+  - DistillationDMLLoss:
+      model_name_pairs:
+      - ["Student", "Student2"]
+      maps_name: "thrink_maps"
+      weight: 1.0
+      act: "softmax"
+      model_name_pairs: ["Student", "Student2"]
+      key: maps
+  - DistillationDBLoss:
+      weight: 1.0
+      model_name_list: ["Student", "Student2"]
+      # key: maps
+      name: DBLoss
+      balance_loss: true
+      main_loss_type: DiceLoss
+      alpha: 5
+      beta: 10
+      ohem_ratio: 3
+
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+    warmup_epoch: 2
+  regularizer:
+    name: 'L2'
+    factor: 0
+
+PostProcess:
+  name: DistillationDBPostProcess
+  model_name: ["Student", "Student2"]
+  key: head_out
+  thresh: 0.3
+  box_thresh: 0.6
+  max_candidates: 1000
+  unclip_ratio: 1.5
+
+Metric:
+  name: DistillationMetric
+  base_metric_name: DetMetric
+  main_indicator: hmean
+  key: "Student"
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/icdar2015/text_localization/
+    label_file_list:
+      - ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
+    ratio_list: [1.0]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - DetLabelEncode: # Class handling label
+      - IaaAugment:
+          augmenter_args:
+            - { 'type': Fliplr, 'args': { 'p': 0.5 } }
+            - { 'type': Affine, 'args': { 'rotate': [-10, 10] } }
+            - { 'type': Resize, 'args': { 'size': [0.5, 3] } }
+      - EastRandomCropData:
+          size: [960, 960]
+          max_tries: 50
+          keep_ratio: true
+      - MakeBorderMap:
+          shrink_ratio: 0.4
+          thresh_min: 0.3
+          thresh_max: 0.7
+      - MakeShrinkMap:
+          shrink_ratio: 0.4
+          min_text_size: 8
+      - NormalizeImage:
+          scale: 1./255.
+          mean: [0.485, 0.456, 0.406]
+          std: [0.229, 0.224, 0.225]
+          order: 'hwc'
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] # the order of the dataloader list
+  loader:
+    shuffle: True
+    drop_last: False
+    batch_size_per_card: 8
+    num_workers: 4
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/icdar2015/text_localization/
+    label_file_list:
+      - ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - DetLabelEncode: # Class handling label
+      - DetResizeForTest:
+#           image_shape: [736, 1280]
+      - NormalizeImage:
+          scale: 1./255.
+          mean: [0.485, 0.456, 0.406]
+          std: [0.229, 0.224, 0.225]
+          order: 'hwc'
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 1 # must be 1
+    num_workers: 2

ppocr/losses/basic_loss.py

@@ -54,6 +54,27 @@ class CELoss(nn.Layer):
         return loss
 
 
+class KLJSLoss(object):
+    def __init__(self, mode='kl'):
+        assert mode in ['kl', 'js', 'KL', 'JS'], "mode can only be one of ['kl', 'js', 'KL', 'JS']"
+        self.mode = mode
+
+    def __call__(self, p1, p2, reduction="mean"):
+
+        loss = paddle.multiply(p2, paddle.log( (p2+1e-5)/(p1+1e-5) + 1e-5))
+
+        if self.mode.lower() == "js":
+            loss += paddle.multiply(p1, paddle.log((p1+1e-5)/(p2+1e-5) + 1e-5))
+            loss *= 0.5
+        if reduction == "mean":
+            loss = paddle.mean(loss, axis=[1,2])
+        elif reduction=="none" or reduction is None:
+            return loss 
+        else:
+            loss = paddle.sum(loss, axis=[1,2])
+
+        return loss 
+
 class DMLLoss(nn.Layer):
     """
     DMLLoss
@@ -69,17 +90,21 @@ class DMLLoss(nn.Layer):
             self.act = nn.Sigmoid()
         else:
             self.act = None
+        
+        self.jskl_loss = KLJSLoss(mode="js")
 
     def forward(self, out1, out2):
         if self.act is not None:
             out1 = self.act(out1)
             out2 = self.act(out2)
-
-        log_out1 = paddle.log(out1)
-        log_out2 = paddle.log(out2)
-        loss = (F.kl_div(
-            log_out1, out2, reduction='batchmean') + F.kl_div(
-                log_out2, out1, reduction='batchmean')) / 2.0
+        if len(out1.shape) < 2:
+            log_out1 = paddle.log(out1)
+            log_out2 = paddle.log(out2)
+            loss = (F.kl_div(
+                log_out1, out2, reduction='batchmean') + F.kl_div(
+                    log_out2, out1, reduction='batchmean')) / 2.0
+        else:
+            loss = self.jskl_loss(out1, out2)
         return loss
 
 

ppocr/losses/combined_loss.py

@@ -17,7 +17,7 @@ import paddle.nn as nn
 
 from .distillation_loss import DistillationCTCLoss
 from .distillation_loss import DistillationDMLLoss
-from .distillation_loss import DistillationDistanceLoss
+from .distillation_loss import DistillationDistanceLoss, DistillationDBLoss, DistillationDilaDBLoss
 
 
 class CombinedLoss(nn.Layer):
@@ -44,15 +44,16 @@ class CombinedLoss(nn.Layer):
 
     def forward(self, input, batch, **kargs):
         loss_dict = {}
+        loss_all = 0.
         for idx, loss_func in enumerate(self.loss_func):
             loss = loss_func(input, batch, **kargs)
             if isinstance(loss, paddle.Tensor):
                 loss = {"loss_{}_{}".format(str(loss), idx): loss}
             weight = self.loss_weight[idx]
-            loss = {
-                "{}_{}".format(key, idx): loss[key] * weight
-                for key in loss
-            }
-            loss_dict.update(loss)
-        loss_dict["loss"] = paddle.add_n(list(loss_dict.values()))
+            for key in loss.keys():
+                if key == "loss":
+                    loss_all += loss[key] * weight
+                else:
+                    loss_dict["{}_{}".format(key, idx)] = loss[key]
+        loss_dict["loss"] = loss_all
         return loss_dict

ppocr/losses/distillation_loss.py

@@ -14,23 +14,76 @@
 
 import paddle
 import paddle.nn as nn
+import numpy as np
+import cv2
 
 from .rec_ctc_loss import CTCLoss
 from .basic_loss import DMLLoss
 from .basic_loss import DistanceLoss
+from .det_db_loss import DBLoss
+from .det_basic_loss import BalanceLoss, MaskL1Loss, DiceLoss
+
+
+def _sum_loss(loss_dict):
+    if "loss" in loss_dict.keys():
+        return loss_dict
+    else:
+        loss_dict["loss"] = 0.
+        for k, value in loss_dict.items():
+            if k == "loss":
+                continue
+            else:
+                loss_dict["loss"] += value
+        return loss_dict
 
 
 class DistillationDMLLoss(DMLLoss):
     """
     """
 
-    def __init__(self, model_name_pairs=[], act=None, key=None,
-                 name="loss_dml"):
+    def __init__(self,
+                 model_name_pairs=[],
+                 act=None,
+                 key=None,
+                 maps_name=None,
+                 name="dml"):
         super().__init__(act=act)
         assert isinstance(model_name_pairs, list)
         self.key = key
-        self.model_name_pairs = model_name_pairs
+        self.model_name_pairs = self._check_model_name_pairs(model_name_pairs)
         self.name = name
+        self.maps_name = self._check_maps_name(maps_name)
+    
+    def _check_model_name_pairs(self, model_name_pairs):
+        if not isinstance(model_name_pairs, list):
+            return []
+        elif isinstance(model_name_pairs[0], list) and isinstance(model_name_pairs[0][0], str):
+            return model_name_pairs
+        else:
+            return [model_name_pairs]
+
+    def _check_maps_name(self, maps_name):
+        if maps_name is None:
+            return None
+        elif type(maps_name) == str:
+            return [maps_name]
+        elif type(maps_name) == list:
+            return [maps_name]
+        else:
+            return None
+
+    def _slice_out(self, outs):
+        new_outs = {}
+        for k in self.maps_name:
+            if k == "thrink_maps":
+                new_outs[k] = outs[:, 0, :, :]
+            elif k == "threshold_maps":
+                new_outs[k] = outs[:, 1, :, :]
+            elif k == "binary_maps":
+                new_outs[k] = outs[:, 2, :, :]
+            else:
+                continue
+        return new_outs
 
     def forward(self, predicts, batch):
         loss_dict = dict()
@@ -40,13 +93,30 @@ class DistillationDMLLoss(DMLLoss):
             if self.key is not None:
                 out1 = out1[self.key]
                 out2 = out2[self.key]
-            loss = super().forward(out1, out2)
-            if isinstance(loss, dict):
-                for key in loss:
-                    loss_dict["{}_{}_{}_{}".format(key, pair[0], pair[1],
-                                                   idx)] = loss[key]
+
+            if self.maps_name is None:
+                loss = super().forward(out1, out2)
+                if isinstance(loss, dict):
+                    for key in loss:
+                        loss_dict["{}_{}_{}_{}".format(key, pair[0], pair[1],
+                                                       idx)] = loss[key]
+                else:
+                    loss_dict["{}_{}".format(self.name, idx)] = loss
             else:
-                loss_dict["{}_{}".format(self.name, idx)] = loss
+                outs1 = self._slice_out(out1)
+                outs2 = self._slice_out(out2)
+                for _c, k in enumerate(outs1.keys()):
+                    loss = super().forward(outs1[k], outs2[k])
+                    if isinstance(loss, dict):
+                        for key in loss:
+                            loss_dict["{}_{}_{}_{}_{}".format(key, pair[
+                                0], pair[1], map_name, idx)] = loss[key]
+                    else:
+                        loss_dict["{}_{}_{}".format(self.name, self.maps_name[_c],
+                                                    idx)] = loss
+        
+        loss_dict = _sum_loss(loss_dict)
+
         return loss_dict
 
 
@@ -73,6 +143,98 @@ class DistillationCTCLoss(CTCLoss):
         return loss_dict
 
 
+class DistillationDBLoss(DBLoss):
+    def __init__(self,
+                 model_name_list=[],
+                 balance_loss=True,
+                 main_loss_type='DiceLoss',
+                 alpha=5,
+                 beta=10,
+                 ohem_ratio=3,
+                 eps=1e-6,
+                 name="db",
+                 **kwargs):
+        super().__init__()
+        self.model_name_list = model_name_list
+        self.name = name
+        self.key = None
+
+    def forward(self, predicts, batch):
+        loss_dict = {}
+        for idx, model_name in enumerate(self.model_name_list):
+            out = predicts[model_name]
+            if self.key is not None:
+                out = out[self.key]
+            loss = super().forward(out, batch)
+
+            if isinstance(loss, dict):
+                for key in loss.keys():
+                    if key == "loss":
+                        continue
+                    name = "{}_{}_{}".format(self.name, model_name, key)
+                    loss_dict[name] = loss[key]
+            else:
+                loss_dict["{}_{}".format(self.name, model_name)] = loss
+
+        loss_dict = _sum_loss(loss_dict)
+        return loss_dict
+
+
+class DistillationDilaDBLoss(DBLoss):
+    def __init__(self,
+                 model_name_pairs=[],
+                 key=None,
+                 balance_loss=True,
+                 main_loss_type='DiceLoss',
+                 alpha=5,
+                 beta=10,
+                 ohem_ratio=3,
+                 eps=1e-6,
+                 name="dila_dbloss"):
+        super().__init__()
+        self.model_name_pairs = model_name_pairs
+        self.name = name
+        self.key = key
+
+    def forward(self, predicts, batch):
+        loss_dict = dict()
+        for idx, pair in enumerate(self.model_name_pairs):
+            stu_outs = predicts[pair[0]]
+            tch_outs = predicts[pair[1]]
+            if self.key is not None:
+                stu_preds = stu_outs[self.key]
+                tch_preds = tch_outs[self.key]
+
+            stu_shrink_maps = stu_preds[:, 0, :, :]
+            stu_binary_maps = stu_preds[:, 2, :, :]
+
+            # dilation to teacher prediction
+            dilation_w = np.array([[1, 1], [1, 1]])
+            th_shrink_maps = tch_preds[:, 0, :, :]
+            th_shrink_maps = th_shrink_maps.numpy() > 0.3  # thresh = 0.3 
+            dilate_maps = np.zeros_like(th_shrink_maps).astype(np.float32)
+            for i in range(th_shrink_maps.shape[0]):
+                dilate_maps[i] = cv2.dilate(
+                    th_shrink_maps[i, :, :].astype(np.uint8), dilation_w)
+            th_shrink_maps = paddle.to_tensor(dilate_maps)
+
+            label_threshold_map, label_threshold_mask, label_shrink_map, label_shrink_mask = batch[
+                1:]
+
+            # calculate the shrink map loss
+            bce_loss = self.alpha * self.bce_loss(
+                stu_shrink_maps, th_shrink_maps, label_shrink_mask)
+            loss_binary_maps = self.dice_loss(stu_binary_maps, th_shrink_maps,
+                                              label_shrink_mask)
+
+            # k = f"{self.name}_{pair[0]}_{pair[1]}"
+            k = "{}_{}_{}".format(self.name, pair[0], pair[1])
+            loss_dict[k] = bce_loss + loss_binary_maps
+
+        loss_dict = _sum_loss(loss_dict)
+        return loss_dict
+
+
 class DistillationDistanceLoss(DistanceLoss):
     """
     """

ppocr/metrics/det_metric.py

@@ -55,6 +55,7 @@ class DetMetric(object):
             result = self.evaluator.evaluate_image(gt_info_list, det_info_list)
             self.results.append(result)
 
+
     def get_metric(self):
         """
         return metrics {

ppocr/metrics/distillation_metric.py

@@ -24,8 +24,8 @@ from .cls_metric import ClsMetric
 class DistillationMetric(object):
     def __init__(self,
                  key=None,
-                 base_metric_name="RecMetric",
-                 main_indicator='acc',
+                 base_metric_name=None,
+                 main_indicator=None,
                  **kwargs):
         self.main_indicator = main_indicator
         self.key = key
@@ -42,16 +42,13 @@ class DistillationMetric(object):
                 main_indicator=self.main_indicator, **self.kwargs)
             self.metrics[key].reset()
 
-    def __call__(self, preds, *args, **kwargs):
+    def __call__(self, preds, batch, **kwargs):
         assert isinstance(preds, dict)
         if self.metrics is None:
             self._init_metrcis(preds)
         output = dict()
         for key in preds:
-            metric = self.metrics[key].__call__(preds[key], *args, **kwargs)
-            for sub_key in metric:
-                output["{}_{}".format(key, sub_key)] = metric[sub_key]
-        return output
+            self.metrics[key].__call__(preds[key], batch, **kwargs)
 
     def get_metric(self):
         """

ppocr/modeling/architectures/base_model.py

@@ -79,7 +79,10 @@ class BaseModel(nn.Layer):
             x = self.neck(x)
         y["neck_out"] = x
         x = self.head(x, targets=data)
-        y["head_out"] = x
+        if isinstance(x, dict):
+            y.update(x)
+        else:
+            y["head_out"] = x
         if self.return_all_feats:
             return y
         else:

ppocr/modeling/architectures/distillation_model.py

@@ -21,7 +21,7 @@ from ppocr.modeling.backbones import build_backbone
 from ppocr.modeling.necks import build_neck
 from ppocr.modeling.heads import build_head
 from .base_model import BaseModel
-from ppocr.utils.save_load import init_model
+from ppocr.utils.save_load import init_model, load_pretrained_params
 
 __all__ = ['DistillationModel']
 
@@ -46,7 +46,7 @@ class DistillationModel(nn.Layer):
                 pretrained = model_config.pop("pretrained")
             model = BaseModel(model_config)
             if pretrained is not None:
-                init_model(model, path=pretrained)
+                model = load_pretrained_params(model, pretrained)
             if freeze_params:
                 for param in model.parameters():
                     param.trainable = False

ppocr/postprocess/__init__.py

@@ -21,7 +21,7 @@ import copy
 
 __all__ = ['build_post_process']
 
-from .db_postprocess import DBPostProcess
+from .db_postprocess import DBPostProcess, DistillationDBPostProcess
 from .east_postprocess import EASTPostProcess
 from .sast_postprocess import SASTPostProcess
 from .rec_postprocess import CTCLabelDecode, AttnLabelDecode, SRNLabelDecode, DistillationCTCLabelDecode, \
@@ -34,7 +34,8 @@ def build_post_process(config, global_config=None):
     support_dict = [
         'DBPostProcess', 'EASTPostProcess', 'SASTPostProcess', 'CTCLabelDecode',
         'AttnLabelDecode', 'ClsPostProcess', 'SRNLabelDecode', 'PGPostProcess',
-        'DistillationCTCLabelDecode', 'TableLabelDecode'
+        'DistillationCTCLabelDecode', 'TableLabelDecode',
+        'DistillationDBPostProcess'
     ]
 
     config = copy.deepcopy(config)

ppocr/postprocess/db_postprocess.py

@@ -187,3 +187,29 @@ class DBPostProcess(object):
 
             boxes_batch.append({'points': boxes})
         return boxes_batch
+
+
+class DistillationDBPostProcess(object):
+    def __init__(self, model_name=["student"],
+                 key=None,
+                 thresh=0.3,
+                 box_thresh=0.6,
+                 max_candidates=1000,
+                 unclip_ratio=1.5,
+                 use_dilation=False,
+                 score_mode="fast",
+                 **kwargs):
+        self.model_name = model_name
+        self.key = key
+        self.post_process = DBPostProcess(thresh=thresh,
+                 box_thresh=box_thresh,
+                 max_candidates=max_candidates,
+                 unclip_ratio=unclip_ratio,
+                 use_dilation=use_dilation,
+                 score_mode=score_mode)
+
+    def __call__(self, predicts, shape_list):
+        results = {}
+        for k in self.model_name:
+            results[k] = self.post_process(predicts[k], shape_list=shape_list)
+        return results

ppocr/utils/save_load.py

@@ -116,6 +116,27 @@ def load_dygraph_params(config, model, logger, optimizer):
         logger.info(f"loaded pretrained_model successful from {pm}")
         return {}
 
+def load_pretrained_params(model, path):
+    if path is None:
+        return False
+    if not os.path.exists(path) and not os.path.exists(path + ".pdparams"):
+        print(f"The pretrained_model {path} does not exists!")
+        return False
+
+    path = path if path.endswith('.pdparams') else path + '.pdparams'
+    params = paddle.load(path)
+    state_dict = model.state_dict()
+    new_state_dict = {}
+    for k1, k2 in zip(state_dict.keys(), params.keys()):
+        if list(state_dict[k1].shape) == list(params[k2].shape):
+            new_state_dict[k1] = params[k2]
+        else:
+            print(
+                f"The shape of model params {k1} {state_dict[k1].shape} not matched with loaded params {k2} {params[k2].shape} !"
+            )
+    model.set_state_dict(new_state_dict)
+    print(f"load pretrain successful from {path}")
+    return model
 
 def save_model(model,
                optimizer,

tools/eval.py

@@ -27,7 +27,7 @@ from ppocr.data import build_dataloader
 from ppocr.modeling.architectures import build_model
 from ppocr.postprocess import build_post_process
 from ppocr.metrics import build_metric
-from ppocr.utils.save_load import init_model
+from ppocr.utils.save_load import init_model, load_pretrained_params
 from ppocr.utils.utility import print_dict
 import tools.program as program
 
@@ -55,7 +55,10 @@ def main():
 
     model = build_model(config['Architecture'])
     use_srn = config['Architecture']['algorithm'] == "SRN"
-    model_type = config['Architecture']['model_type']
+    if "model_type" in config['Architecture'].keys():
+        model_type = config['Architecture']['model_type']
+    else:
+        model_type = None
 
     best_model_dict = init_model(config, model)
     if len(best_model_dict):
@@ -68,7 +71,7 @@ def main():
 
     # start eval
     metric = program.eval(model, valid_dataloader, post_process_class,
-                          eval_class, model_type, use_srn)
+                        eval_class, model_type, use_srn)
     logger.info('metric eval ***************')
     for k, v in metric.items():
         logger.info('{}:{}'.format(k, v))

tools/program.py

@@ -186,7 +186,10 @@ def train(config,
     model.train()
 
     use_srn = config['Architecture']['algorithm'] == "SRN"
-    model_type = config['Architecture']['model_type']
+    try: 
+        model_type = config['Architecture']['model_type']
+    except: 
+        model_type = None
 
     if 'start_epoch' in best_model_dict:
         start_epoch = best_model_dict['start_epoch']

tools/train.py

@@ -98,7 +98,6 @@ def main(config, device, logger, vdl_writer):
     eval_class = build_metric(config['Metric'])
     # load pretrain model
     pre_best_model_dict = load_dygraph_params(config, model, logger, optimizer)
-
     logger.info('train dataloader has {} iters'.format(len(train_dataloader)))
     if valid_dataloader is not None:
         logger.info('valid dataloader has {} iters'.format(