code style fix

docs/source/models/dssm_derivatives.md

@@ -1,12 +1,12 @@
-# DSSM衍生扩展模型 
+# DSSM衍生扩展模型
 
 ## DSSM + SENet
+
 ### 简介
 
 在推荐场景中，往往存在多种用户特征和物品特征，特征类型各不相同，各种特征经过embedding层后进入双塔模型的DNN层进行训练，在部分场景中甚至还会引入多模态embedding特征, 如图像和文本的embedding。
 然而各个特征对目标的影响不尽相同，有的特征重要性高，对模型整体表现影响大，有的特征则影响较小。因此当特征不断增多时，可以结合SENet自动学习每个特征的权重，增强重要信息到塔顶的能力。
 
-
 ![dssm+senet](../../images/models/dssm+senet.png)
 
 ### 配置说明
@@ -70,12 +70,14 @@ model_config:{
 }
 ```
 
-- senet参数配置: 
+- senet参数配置:
   - num_squeeze_group: 每个特征embedding的分组个数， 默认为2
   - reduction_ratio: 维度压缩比例， 默认为4
 
 ### 示例Config
+
 [dssm_senet_on_taobao.config](https://github.com/alibaba/EasyRec/tree/master/examples/configs/dssm_senet_on_taobao.config)
 
 ### 参考论文
-[Squeeze-and-Excitation Networks](https://arxiv.org/abs/1709.01507)
+
+[Squeeze-and-Excitation Networks](https://arxiv.org/abs/1709.01507)

easy_rec/python/layers/senet.py

@@ -7,8 +7,7 @@
 
 
 class SENet:
-  '''
-  Squeeze and Excite Network
+  """Squeeze and Excite Network.
 
   Input shape
   - A list of 2D tensor with shape: ``(batch_size,embedding_size)``.
@@ -20,15 +19,20 @@ class SENet:
     reduction_ratio: int, reduction ratio for squeeze.
     l2_reg: float, l2 regularizer for embedding.
     name: str, name of the layer.
+  """
 
-  '''
-  def __init__(self, num_fields, num_squeeze_group, reduction_ratio, l2_reg, name='SENet'):
+  def __init__(self,
+               num_fields,
+               num_squeeze_group,
+               reduction_ratio,
+               l2_reg,
+               name='SENet'):
     self.num_fields = num_fields
     self.num_squeeze_group = num_squeeze_group
     self.reduction_ratio = reduction_ratio
     self._l2_reg = l2_reg
     self._name = name
-  
+
   def __call__(self, inputs):
     g = self.num_squeeze_group
     f = self.num_fields
@@ -39,7 +43,6 @@ def __call__(self, inputs):
     for input in inputs:
       emb_size += int(input.shape[-1])
 
-
     group_embs = [
         tf.reshape(emb, [-1, g, int(emb.shape[-1]) // g]) for emb in inputs
     ]
@@ -50,24 +53,21 @@ def __call__(self, inputs):
       squeezed.append(tf.reduce_mean(emb, axis=-1))  # [B, g]
     z = tf.concat(squeezed, axis=1)  # [bs, field_size * num_groups * 2]
 
-
-
     reduced = tf.layers.dense(
-          inputs=z,
-          units=reduction_size,
-          kernel_regularizer=self._l2_reg,
-          activation='relu',
-          name='%s/reduce' % self._name)
-    
+        inputs=z,
+        units=reduction_size,
+        kernel_regularizer=self._l2_reg,
+        activation='relu',
+        name='%s/reduce' % self._name)
+
     excited_weights = tf.layers.dense(
         inputs=reduced,
-        units=emb_size, 
-        kernel_initializer='glorot_normal', 
+        units=emb_size,
+        kernel_initializer='glorot_normal',
         name='%s/excite' % self._name)
 
-
     # Re-weight
     inputs = tf.concat(inputs, axis=-1)
     output = inputs * excited_weights
 
-    return output
+    return output

easy_rec/python/model/dssm_senet.py

@@ -3,13 +3,14 @@
 import tensorflow as tf
 
 from easy_rec.python.layers import dnn
+from easy_rec.python.layers import senet
+from easy_rec.python.model.dssm import DSSM
 from easy_rec.python.model.match_model import MatchModel
-from easy_rec.python.protos.dssm_senet_pb2 import DSSM_SENet as DSSM_SENet_Config
 from easy_rec.python.protos.loss_pb2 import LossType
 from easy_rec.python.protos.simi_pb2 import Similarity
 from easy_rec.python.utils.proto_util import copy_obj
-from easy_rec.python.layers import senet
-from easy_rec.python.model.dssm import DSSM  
+
+from easy_rec.python.protos.dssm_senet_pb2 import DSSM_SENet as DSSM_SENet_Config
 
 if tf.__version__ >= '2.0':
   tf = tf.compat.v1
@@ -25,7 +26,8 @@ def __init__(self,
                labels=None,
                is_training=False):
 
-    MatchModel.__init__(self, model_config, feature_configs, features, labels, is_training)
+    MatchModel.__init__(self, model_config, feature_configs, features, labels,
+                        is_training)
 
     assert self._model_config.WhichOneof('model') == 'dssm_senet', \
         'invalid model config: %s' % self._model_config.WhichOneof('model')
@@ -35,26 +37,27 @@ def __init__(self,
     # copy_obj so that any modification will not affect original config
     self.user_tower = copy_obj(self._model_config.user_tower)
 
-    self.user_seq_features, self.user_plain_features, self.user_feature_list = self._input_layer(self._feature_dict, 'user', is_combine=False)
+    self.user_seq_features, self.user_plain_features, self.user_feature_list = self._input_layer(
+        self._feature_dict, 'user', is_combine=False)
     self.user_num_fields = len(self.user_feature_list)
 
     # copy_obj so that any modification will not affect original config
     self.item_tower = copy_obj(self._model_config.item_tower)
 
-    self.item_seq_features, self.item_plain_features, self.item_feature_list = self._input_layer(self._feature_dict, 'item', is_combine=False)
+    self.item_seq_features, self.item_plain_features, self.item_feature_list = self._input_layer(
+        self._feature_dict, 'item', is_combine=False)
     self.item_num_fields = len(self.item_feature_list)
 
     self._user_tower_emb = None
     self._item_tower_emb = None
 
   def build_predict_graph(self):
     user_senet = senet.SENet(
-      num_fields=self.user_num_fields, 
-      num_squeeze_group=self.user_tower.senet.num_squeeze_group, 
-      reduction_ratio=self.user_tower.senet.reduction_ratio, 
-      l2_reg=self._l2_reg, 
-      name='user_senet'
-    )
+        num_fields=self.user_num_fields,
+        num_squeeze_group=self.user_tower.senet.num_squeeze_group,
+        reduction_ratio=self.user_tower.senet.reduction_ratio,
+        l2_reg=self._l2_reg,
+        name='user_senet')
     user_senet_output_list = user_senet(self.user_feature_list)
     user_senet_output = tf.concat(user_senet_output_list, axis=-1)
 
@@ -70,15 +73,14 @@ def build_predict_graph(self):
         name='user_dnn/dnn_%d' % (num_user_dnn_layer - 1))
 
     item_senet = senet.SENet(
-      num_fields=self.item_num_fields, 
-      num_squeeze_group=self.item_tower.senet.num_squeeze_group, 
-      reduction_ratio=self.item_tower.senet.reduction_ratio, 
-      l2_reg=self._l2_reg, 
-      name='item_senet'
-    )
-
+        num_fields=self.item_num_fields,
+        num_squeeze_group=self.item_tower.senet.num_squeeze_group,
+        reduction_ratio=self.item_tower.senet.reduction_ratio,
+        l2_reg=self._l2_reg,
+        name='item_senet')
+
     item_senet_output_list = item_senet(self.item_feature_list)
-    item_senet_output = tf.concat(item_senet_output_list, axis=-1) 
+    item_senet_output = tf.concat(item_senet_output_list, axis=-1)
 
     num_item_dnn_layer = len(self.item_tower.dnn.hidden_units)
     last_item_hidden = self.item_tower.dnn.hidden_units.pop()
@@ -137,5 +139,5 @@ def build_predict_graph(self):
 
   def build_output_dict(self):
     output_dict = MatchModel.build_output_dict(self)
-    
-    return output_dict
+
+    return output_dict

easy_rec/python/protos/dssm_senet.proto

@@ -9,7 +9,7 @@ message DSSM_SENet_Tower {
   required string id = 1;
   required SENet senet = 2;
   required DNN dnn = 3;
-  
+
 };