add distillation function

ppocr/losses/__init__.py

@@ -13,28 +13,37 @@
 # limitations under the License.
 
 import copy
+import paddle
+import paddle.nn as nn
 
+# det loss
+from .det_db_loss import DBLoss
+from .det_east_loss import EASTLoss
+from .det_sast_loss import SASTLoss
 
-def build_loss(config):
-    # det loss
-    from .det_db_loss import DBLoss
-    from .det_east_loss import EASTLoss
-    from .det_sast_loss import SASTLoss
+# rec loss
+from .rec_ctc_loss import CTCLoss
+from .rec_att_loss import AttentionLoss
+from .rec_srn_loss import SRNLoss
+
+# cls loss
+from .cls_loss import ClsLoss
+
+# e2e loss
+from .e2e_pg_loss import PGLoss
 
-    # rec loss
-    from .rec_ctc_loss import CTCLoss
-    from .rec_att_loss import AttentionLoss
-    from .rec_srn_loss import SRNLoss
+# basic loss function
+from .basic_loss import DistanceLoss
 
-    # cls loss
-    from .cls_loss import ClsLoss
+# combined loss function
+from .combined_loss import CombinedLoss
 
-    # e2e loss
-    from .e2e_pg_loss import PGLoss
+
+def build_loss(config):
     support_dict = [
         'DBLoss', 'EASTLoss', 'SASTLoss', 'CTCLoss', 'ClsLoss', 'AttentionLoss',
-        'SRNLoss', 'PGLoss']
-
+        'SRNLoss', 'PGLoss', 'CombinedLoss'
+    ]
     config = copy.deepcopy(config)
     module_name = config.pop('name')
     assert module_name in support_dict, Exception('loss only support {}'.format(

ppocr/losses/basic_loss.py

+#copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
+#
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
+
+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+
+from paddle.nn import L1Loss
+from paddle.nn import MSELoss as L2Loss
+from paddle.nn import SmoothL1Loss
+
+
+class CELoss(nn.Layer):
+    def __init__(self, name="loss_ce", epsilon=None):
+        super().__init__()
+        self.name = name
+        if epsilon is not None and (epsilon <= 0 or epsilon >= 1):
+            epsilon = None
+        self.epsilon = epsilon
+
+    def _labelsmoothing(self, target, class_num):
+        if target.shape[-1] != class_num:
+            one_hot_target = F.one_hot(target, class_num)
+        else:
+            one_hot_target = target
+        soft_target = F.label_smooth(one_hot_target, epsilon=self.epsilon)
+        soft_target = paddle.reshape(soft_target, shape=[-1, class_num])
+        return soft_target
+
+    def forward(self, x, label):
+        loss_dict = {}
+        if self.epsilon is not None:
+            class_num = x.shape[-1]
+            label = self._labelsmoothing(label, class_num)
+            x = -F.log_softmax(x, axis=-1)
+            loss = paddle.sum(x * label, axis=-1)
+        else:
+            if label.shape[-1] == x.shape[-1]:
+                label = F.softmax(label, axis=-1)
+                soft_label = True
+            else:
+                soft_label = False
+            loss = F.cross_entropy(x, label=label, soft_label=soft_label)
+
+        loss_dict[self.name] = paddle.mean(loss)
+        return loss_dict
+
+
+class DMLLoss(nn.Layer):
+    """
+    DMLLoss
+    """
+
+    def __init__(self, name="loss_dml"):
+        super().__init__()
+        self.name = name
+
+    def forward(self, out1, out2):
+        loss_dict = {}
+        soft_out1 = F.softmax(out1, axis=-1)
+        log_soft_out1 = paddle.log(soft_out1)
+        soft_out2 = F.softmax(out2, axis=-1)
+        log_soft_out2 = paddle.log(soft_out2)
+        loss = (F.kl_div(
+            log_soft_out1, soft_out2, reduction='batchmean') + F.kl_div(
+                log_soft_out2, soft_out1, reduction='batchmean')) / 2.0
+        loss_dict[self.name] = loss
+        return loss_dict
+
+
+class DistanceLoss(nn.Layer):
+    """
+    DistanceLoss:
+        mode: loss mode
+        name: loss key in the output dict
+    """
+
+    def __init__(self, mode="l2", name="loss_dist", **kargs):
+        assert mode in ["l1", "l2", "smooth_l1"]
+        if mode == "l1":
+            self.loss_func = nn.L1Loss(**kargs)
+        elif mode == "l1":
+            self.loss_func = nn.MSELoss(**kargs)
+        elif mode == "smooth_l1":
+            self.loss_func = nn.SmoothL1Loss(**kargs)
+
+        self.name = "{}_{}".format(name, mode)
+
+    def forward(self, x, y):
+        return {self.name: self.loss_func(x, y)}

ppocr/losses/cls_loss.py

@@ -24,7 +24,7 @@ class ClsLoss(nn.Layer):
         super(ClsLoss, self).__init__()
         self.loss_func = nn.CrossEntropyLoss(reduction='mean')
 
-    def __call__(self, predicts, batch):
+    def forward(self, predicts, batch):
         label = batch[1]
         loss = self.loss_func(input=predicts, label=label)
         return {'loss': loss}

ppocr/losses/combined_loss.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle
+import paddle.nn as nn
+
+from .distillation_loss import DistillationCTCLoss
+from .distillation_loss import DistillationDMLLoss
+
+
+class CombinedLoss(nn.Layer):
+    """
+    CombinedLoss:
+        a combionation of loss function
+    """
+
+    def __init__(self, loss_config_list=None):
+        super().__init__()
+        self.loss_func = []
+        self.loss_weight = []
+        assert isinstance(loss_config_list, list), (
+            'operator config should be a list')
+        for config in loss_config_list:
+            assert isinstance(config,
+                              dict) and len(config) == 1, "yaml format error"
+            name = list(config)[0]
+            param = config[name]
+            assert "weight" in param, "weight must be in param, but param just contains {}".format(
+                param.keys())
+            self.loss_weight.append(param.pop("weight"))
+            self.loss_func.append(eval(name)(**param))
+
+    def forward(self, input, batch, **kargs):
+        loss_dict = {}
+        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()))
+        return loss_dict

ppocr/losses/distillation_loss.py

+#copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
+#
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
+
+import paddle
+import paddle.nn as nn
+
+from .rec_ctc_loss import CTCLoss
+from .basic_loss import DMLLoss
+
+
+class DistillationDMLLoss(DMLLoss):
+    """
+    """
+
+    def __init__(self,
+                 model_name_list1=[],
+                 model_name_list2=[],
+                 key=None,
+                 name="loss_dml"):
+        super().__init__(name=name)
+        if not isinstance(model_name_list1, (list, )):
+            model_name_list1 = [model_name_list1]
+        if not isinstance(model_name_list2, (list, )):
+            model_name_list2 = [model_name_list2]
+
+        assert len(model_name_list1) == len(model_name_list2)
+        self.model_name_list1 = model_name_list1
+        self.model_name_list2 = model_name_list2
+        self.key = key
+
+    def forward(self, predicts, batch):
+        loss_dict = dict()
+        for idx in range(len(self.model_name_list1)):
+            out1 = predicts[self.model_name_list1[idx]]
+            out2 = predicts[self.model_name_list2[idx]]
+            if self.key is not None:
+                out1 = out1[self.key]
+                out2 = out2[self.key]
+            loss = super().forward(out1, out2)
+            if isinstance(loss, dict):
+                assert len(loss) == 1
+                loss = list(loss.values())[0]
+            loss_dict["{}_{}".format(self.name, idx)] = loss
+        return loss_dict
+
+
+class DistillationCTCLoss(CTCLoss):
+    def __init__(self, model_name_list=[], key=None, name="loss_ctc"):
+        super().__init__()
+        self.model_name_list = model_name_list
+        self.key = key
+        self.name = name
+
+    def forward(self, predicts, batch):
+        loss_dict = dict()
+        for model_name in 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):
+                assert len(loss) == 1
+                loss = list(loss.values())[0]
+            loss_dict["{}_{}".format(self.name, model_name)] = loss
+        return loss_dict

ppocr/losses/rec_ctc_loss.py

@@ -25,7 +25,7 @@ class CTCLoss(nn.Layer):
         super(CTCLoss, self).__init__()
         self.loss_func = nn.CTCLoss(blank=0, reduction='none')
 
-    def __call__(self, predicts, batch):
+    def forward(self, predicts, batch):
         predicts = predicts.transpose((1, 0, 2))
         N, B, _ = predicts.shape
         preds_lengths = paddle.to_tensor([N] * B, dtype='int64')

ppocr/modeling/architectures/__init__.py

@@ -13,12 +13,20 @@
 # limitations under the License.
 
 import copy
+import importlib
+
+from .base_model import BaseModel
+from .distillation_model import DistillationModel
 
 __all__ = ['build_model']
 
-def build_model(config):
-    from .base_model import BaseModel
 
+def build_model(config):
     config = copy.deepcopy(config)
-    module_class = BaseModel(config)
-    return module_class
\ No newline at end of file
+    if not "name" in config:
+        arch = BaseModel(config)
+    else:
+        name = config.pop("name")
+        mod = importlib.import_module(__name__)
+        arch = getattr(mod, name)(config)
+    return arch

ppocr/modeling/architectures/base_model.py

@@ -32,7 +32,6 @@ class BaseModel(nn.Layer):
             config (dict): the super parameters for module.
         """
         super(BaseModel, self).__init__()
-
         in_channels = config.get('in_channels', 3)
         model_type = config['model_type']
         # build transfrom,

ppocr/modeling/architectures/distillation_model.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from paddle import nn
+from ppocr.modeling.transforms import build_transform
+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 load_dygraph_pretrain
+
+__all__ = ['DistillationModel']
+
+
+class DistillationModel(nn.Layer):
+    def __init__(self, config):
+        """
+        the module for OCR distillation.
+        args:
+            config (dict): the super parameters for module.
+        """
+        super().__init__()
+
+        freeze_params = config["freeze_params"]
+        pretrained = config["pretrained"]
+        if not isinstance(freeze_params, list):
+            freeze_params = [freeze_params]
+        assert len(config["Models"]) == len(freeze_params)
+
+        if not isinstance(pretrained, list):
+            pretrained = [pretrained] * len(config["Models"])
+        assert len(config["Models"]) == len(pretrained)
+
+        self.model_dict = dict()
+        index = 0
+        for key in config["Models"]:
+            model_config = config["Models"][key]
+            model = BaseModel(model_config)
+            if pretrained[index] is not None:
+                load_dygraph_pretrain(model, path=pretrained[index])
+            if freeze_params[index]:
+                for param in model.parameters():
+                    param.trainable = False
+            self.model_dict[key] = self.add_sublayer(key, model)
+            index += 1
+
+    def forward(self, x):
+        result_dict = dict()
+        for key in self.model_dict:
+            result_dict[key] = self.model_dict[key](x)
+        return result_dict

ppocr/modeling/backbones/det_mobilenet_v3.py

@@ -102,8 +102,7 @@ class MobileNetV3(nn.Layer):
             padding=1,
             groups=1,
             if_act=True,
-            act='hardswish',
-            name='conv1')
+            act='hardswish')
 
         self.stages = []
         self.out_channels = []
@@ -125,8 +124,7 @@ class MobileNetV3(nn.Layer):
                     kernel_size=k,
                     stride=s,
                     use_se=se,
-                    act=nl,
-                    name="conv" + str(i + 2)))
+                    act=nl))
             inplanes = make_divisible(scale * c)
             i += 1
         block_list.append(
@@ -138,8 +136,7 @@ class MobileNetV3(nn.Layer):
                 padding=0,
                 groups=1,
                 if_act=True,
-                act='hardswish',
-                name='conv_last'))
+                act='hardswish'))
         self.stages.append(nn.Sequential(*block_list))
         self.out_channels.append(make_divisible(scale * cls_ch_squeeze))
         for i, stage in enumerate(self.stages):
@@ -163,8 +160,7 @@ class ConvBNLayer(nn.Layer):
                  padding,
                  groups=1,
                  if_act=True,
-                 act=None,
-                 name=None):
+                 act=None):
         super(ConvBNLayer, self).__init__()
         self.if_act = if_act
         self.act = act
@@ -175,16 +171,9 @@ class ConvBNLayer(nn.Layer):
             stride=stride,
             padding=padding,
             groups=groups,
-            weight_attr=ParamAttr(name=name + '_weights'),
             bias_attr=False)
 
-        self.bn = nn.BatchNorm(
-            num_channels=out_channels,
-            act=None,
-            param_attr=ParamAttr(name=name + "_bn_scale"),
-            bias_attr=ParamAttr(name=name + "_bn_offset"),
-            moving_mean_name=name + "_bn_mean",
-            moving_variance_name=name + "_bn_variance")
+        self.bn = nn.BatchNorm(num_channels=out_channels, act=None)
 
     def forward(self, x):
         x = self.conv(x)
@@ -209,8 +198,7 @@ class ResidualUnit(nn.Layer):
                  kernel_size,
                  stride,
                  use_se,
-                 act=None,
-                 name=''):
+                 act=None):
         super(ResidualUnit, self).__init__()
         self.if_shortcut = stride == 1 and in_channels == out_channels
         self.if_se = use_se
@@ -222,8 +210,7 @@ class ResidualUnit(nn.Layer):
             stride=1,
             padding=0,
             if_act=True,
-            act=act,
-            name=name + "_expand")
+            act=act)
         self.bottleneck_conv = ConvBNLayer(
             in_channels=mid_channels,
             out_channels=mid_channels,
@@ -232,10 +219,9 @@ class ResidualUnit(nn.Layer):
             padding=int((kernel_size - 1) // 2),
             groups=mid_channels,
             if_act=True,
-            act=act,
-            name=name + "_depthwise")
+            act=act)
         if self.if_se:
-            self.mid_se = SEModule(mid_channels, name=name + "_se")
+            self.mid_se = SEModule(mid_channels)
         self.linear_conv = ConvBNLayer(
             in_channels=mid_channels,
             out_channels=out_channels,
@@ -243,8 +229,7 @@ class ResidualUnit(nn.Layer):
             stride=1,
             padding=0,
             if_act=False,
-            act=None,
-            name=name + "_linear")
+            act=None)
 
     def forward(self, inputs):
         x = self.expand_conv(inputs)
@@ -258,7 +243,7 @@ class ResidualUnit(nn.Layer):
 
 
 class SEModule(nn.Layer):
-    def __init__(self, in_channels, reduction=4, name=""):
+    def __init__(self, in_channels, reduction=4):
         super(SEModule, self).__init__()
         self.avg_pool = nn.AdaptiveAvgPool2D(1)
         self.conv1 = nn.Conv2D(
@@ -266,17 +251,13 @@ class SEModule(nn.Layer):
             out_channels=in_channels // reduction,
             kernel_size=1,
             stride=1,
-            padding=0,
-            weight_attr=ParamAttr(name=name + "_1_weights"),
-            bias_attr=ParamAttr(name=name + "_1_offset"))
+            padding=0)
         self.conv2 = nn.Conv2D(
             in_channels=in_channels // reduction,
             out_channels=in_channels,
             kernel_size=1,
             stride=1,
-            padding=0,
-            weight_attr=ParamAttr(name + "_2_weights"),
-            bias_attr=ParamAttr(name=name + "_2_offset"))
+            padding=0)
 
     def forward(self, inputs):
         outputs = self.avg_pool(inputs)

ppocr/modeling/backbones/rec_mobilenet_v3.py

@@ -96,8 +96,7 @@ class MobileNetV3(nn.Layer):
             padding=1,
             groups=1,
             if_act=True,
-            act='hardswish',
-            name='conv1')
+            act='hardswish')
         i = 0
         block_list = []
         inplanes = make_divisible(inplanes * scale)
@@ -110,8 +109,7 @@ class MobileNetV3(nn.Layer):
                     kernel_size=k,
                     stride=s,
                     use_se=se,
-                    act=nl,
-                    name='conv' + str(i + 2)))
+                    act=nl))
             inplanes = make_divisible(scale * c)
             i += 1
         self.blocks = nn.Sequential(*block_list)
@@ -124,8 +122,7 @@ class MobileNetV3(nn.Layer):
             padding=0,
             groups=1,
             if_act=True,
-            act='hardswish',
-            name='conv_last')
+            act='hardswish')
 
         self.pool = nn.MaxPool2D(kernel_size=2, stride=2, padding=0)
         self.out_channels = make_divisible(scale * cls_ch_squeeze)

ppocr/modeling/heads/rec_ctc_head.py

@@ -23,14 +23,12 @@ from paddle import ParamAttr, nn
 from paddle.nn import functional as F
 
 
-def get_para_bias_attr(l2_decay, k, name):
+def get_para_bias_attr(l2_decay, k):
     regularizer = paddle.regularizer.L2Decay(l2_decay)
     stdv = 1.0 / math.sqrt(k * 1.0)
     initializer = nn.initializer.Uniform(-stdv, stdv)
-    weight_attr = ParamAttr(
-        regularizer=regularizer, initializer=initializer, name=name + "_w_attr")
-    bias_attr = ParamAttr(
-        regularizer=regularizer, initializer=initializer, name=name + "_b_attr")
+    weight_attr = ParamAttr(regularizer=regularizer, initializer=initializer)
+    bias_attr = ParamAttr(regularizer=regularizer, initializer=initializer)
     return [weight_attr, bias_attr]
 
 
@@ -38,13 +36,12 @@ class CTCHead(nn.Layer):
     def __init__(self, in_channels, out_channels, fc_decay=0.0004, **kwargs):
         super(CTCHead, self).__init__()
         weight_attr, bias_attr = get_para_bias_attr(
-            l2_decay=fc_decay, k=in_channels, name='ctc_fc')
+            l2_decay=fc_decay, k=in_channels)
         self.fc = nn.Linear(
             in_channels,
             out_channels,
             weight_attr=weight_attr,
-            bias_attr=bias_attr,
-            name='ctc_fc')
+            bias_attr=bias_attr)
         self.out_channels = out_channels
 
     def forward(self, x, labels=None):

ppocr/postprocess/__init__.py

@@ -21,18 +21,19 @@ import copy
 
 __all__ = ['build_post_process']
 
+from .db_postprocess import DBPostProcess
+from .east_postprocess import EASTPostProcess
+from .sast_postprocess import SASTPostProcess
+from .rec_postprocess import CTCLabelDecode, AttnLabelDecode, SRNLabelDecode, DistillationCTCLabelDecode
+from .cls_postprocess import ClsPostProcess
+from .pg_postprocess import PGPostProcess
 
-def build_post_process(config, global_config=None):
-    from .db_postprocess import DBPostProcess
-    from .east_postprocess import EASTPostProcess
-    from .sast_postprocess import SASTPostProcess
-    from .rec_postprocess import CTCLabelDecode, AttnLabelDecode, SRNLabelDecode
-    from .cls_postprocess import ClsPostProcess
-    from .pg_postprocess import PGPostProcess
 
+def build_post_process(config, global_config=None):
     support_dict = [
         'DBPostProcess', 'EASTPostProcess', 'SASTPostProcess', 'CTCLabelDecode',
-        'AttnLabelDecode', 'ClsPostProcess', 'SRNLabelDecode', 'PGPostProcess'
+        'AttnLabelDecode', 'ClsPostProcess', 'SRNLabelDecode', 'PGPostProcess',
+        'DistillationCTCLabelDecode'
     ]
 
     config = copy.deepcopy(config)

ppocr/postprocess/rec_postprocess.py

@@ -125,6 +125,31 @@ class CTCLabelDecode(BaseRecLabelDecode):
         return dict_character
 
 
+class DistillationCTCLabelDecode(CTCLabelDecode):
+    """
+    Convert 
+    Convert between text-label and text-index
+    """
+
+    def __init__(self,
+                 character_dict_path=None,
+                 character_type='ch',
+                 use_space_char=False,
+                 model_name="student",
+                 key_out=None,
+                 **kwargs):
+        super(DistillationCTCLabelDecode, self).__init__(
+            character_dict_path, character_type, use_space_char)
+        self.model_name = model_name
+        self.key_out = key_out
+
+    def __call__(self, preds, label=None, *args, **kwargs):
+        pred = preds[self.model_name]
+        if self.key_out is not None:
+            pred = pred[self.key_out]
+        return super().__call__(pred, label=label, *args, **kwargs)
+
+
 class AttnLabelDecode(BaseRecLabelDecode):
     """ Convert between text-label and text-index """
 

ppocr/utils/save_load.py

@@ -42,7 +42,10 @@ def _mkdir_if_not_exist(path, logger):
                 raise OSError('Failed to mkdir {}'.format(path))
 
 
-def load_dygraph_pretrain(model, logger, path=None, load_static_weights=False):
+def load_dygraph_pretrain(model,
+                          logger=None,
+                          path=None,
+                          load_static_weights=False):
     if not (os.path.isdir(path) or os.path.exists(path + '.pdparams')):
         raise ValueError("Model pretrain path {} does not "
                          "exists.".format(path))

tools/program.py

@@ -386,7 +386,7 @@ def preprocess(is_train=False):
     alg = config['Architecture']['algorithm']
     assert alg in [
         'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN',
-        'CLS', 'PGNet'
+        'CLS', 'PGNet', 'Distillation'
     ]
 
     device = 'gpu:{}'.format(dist.ParallelEnv().dev_id) if use_gpu else 'cpu'

tools/train.py

@@ -72,7 +72,14 @@ def main(config, device, logger, vdl_writer):
     # for rec algorithm
     if hasattr(post_process_class, 'character'):
         char_num = len(getattr(post_process_class, 'character'))
+        if config['Architecture']["algorithm"] in ["Distillation",
+                                                   ]:  # distillation model
+            for key in config['Architecture']["Models"]:
+                config['Architecture']["Models"][key]["Head"][
+                    'out_channels'] = char_num
+        else:  # base rec model
             config['Architecture']["Head"]['out_channels'] = char_num
+
     model = build_model(config['Architecture'])
     if config['Global']['distributed']:
         model = paddle.DataParallel(model)