Merge branch 'tdm_infer' into 'develop'

add tdm infer

See merge request !17

fleet_rec/core/trainers/transpiler_trainer.py

@@ -48,12 +48,13 @@ class TranspileTrainer(Trainer):
 
         batch_size = envs.get_global_env("batch_size", None, namespace)
         reader_class = envs.get_global_env("class", None, namespace)
+        print("batch_size: {}".format(batch_size))
+        reader = dataloader_instance.dataloader(
+            reader_class, state, self._config_yaml)
 
-        reader = dataloader_instance.dataloader(reader_class, state, self._config_yaml)
-            
         reader_class = envs.lazy_instance_by_fliename(reader_class, class_name)
         reader_ins = reader_class(self._config_yaml)
-        if hasattr(reader_ins,'generate_batch_from_trainfiles'):
+        if hasattr(reader_ins, 'generate_batch_from_trainfiles'):
             dataloader.set_sample_list_generator(reader)
         else:
             dataloader.set_sample_generator(reader, batch_size)
@@ -63,23 +64,27 @@ class TranspileTrainer(Trainer):
         if state == "TRAIN":
             inputs = self.model.get_inputs()
             namespace = "train.reader"
-            train_data_path = envs.get_global_env("train_data_path", None, namespace)
+            train_data_path = envs.get_global_env(
+                "train_data_path", None, namespace)
         else:
             inputs = self.model.get_infer_inputs()
             namespace = "evaluate.reader"
-            train_data_path = envs.get_global_env("test_data_path", None, namespace)
+            train_data_path = envs.get_global_env(
+                "test_data_path", None, namespace)
 
         threads = int(envs.get_runtime_environ("train.trainer.threads"))
         batch_size = envs.get_global_env("batch_size", None, namespace)
         reader_class = envs.get_global_env("class", None, namespace)
         abs_dir = os.path.dirname(os.path.abspath(__file__))
         reader = os.path.join(abs_dir, '../utils', 'dataset_instance.py')
-        pipe_cmd = "python {} {} {} {}".format(reader, reader_class, state, self._config_yaml)
+        pipe_cmd = "python {} {} {} {}".format(
+            reader, reader_class, state, self._config_yaml)
 
         if train_data_path.startswith("fleetrec::"):
             package_base = envs.get_runtime_environ("PACKAGE_BASE")
             assert package_base is not None
-            train_data_path = os.path.join(package_base, train_data_path.split("::")[1])
+            train_data_path = os.path.join(
+                package_base, train_data_path.split("::")[1])
 
         dataset = fluid.DatasetFactory().create_dataset()
         dataset.set_use_var(inputs)
@@ -105,7 +110,8 @@ class TranspileTrainer(Trainer):
             return epoch_id % epoch_interval == 0
 
         def save_inference_model():
-            save_interval = envs.get_global_env("save.inference.epoch_interval", -1, namespace)
+            save_interval = envs.get_global_env(
+                "save.inference.epoch_interval", -1, namespace)
 
             if not need_save(epoch_id, save_interval, False):
                 return
@@ -127,16 +133,19 @@ class TranspileTrainer(Trainer):
             if is_fleet:
                 fleet.save_inference_model(self._exe, dirname, feed_varnames, fetch_vars)
             else:
-                fluid.io.save_inference_model(dirname, feed_varnames, fetch_vars, self._exe)
+                fluid.io.save_inference_model(
+                    dirname, feed_varnames, fetch_vars, self._exe)
             self.inference_models.append((epoch_id, dirname))
 
         def save_persistables():
-            save_interval = envs.get_global_env("save.increment.epoch_interval", -1, namespace)
+            save_interval = envs.get_global_env(
+                "save.increment.epoch_interval", -1, namespace)
 
             if not need_save(epoch_id, save_interval, False):
                 return
 
-            dirname = envs.get_global_env("save.increment.dirname", None, namespace)
+            dirname = envs.get_global_env(
+                "save.increment.dirname", None, namespace)
 
             assert dirname is not None
             dirname = os.path.join(dirname, str(epoch_id))
@@ -149,7 +158,6 @@ class TranspileTrainer(Trainer):
 
         save_persistables()
         save_inference_model()
-        
 
     def instance(self, context):
         models = envs.get_global_env("train.model.models")

models/recall/tdm/config.yaml

@@ -17,7 +17,7 @@ train:
     # for cluster training
     strategy: "async"
 
-  epochs: 4
+  epochs: 2
   workspace: "fleetrec.models.recall.tdm"
 
   reader:
@@ -65,9 +65,16 @@ train:
   save:
     increment:
       dirname: "increment"
-      epoch_interval: 2
+      epoch_interval: 1
       save_last: True
     inference:
       dirname: "inference"
-      epoch_interval: 4
+      epoch_interval: 10
       save_last: True
+
+evaluate:
+  workspace: "fleetrec.models.recall.tdm"
+  reader:
+    batch_size: 1
+    class: "{workspace}/tdm_evaluate_reader.py"
+    test_data_path: "{workspace}/data/test"

models/recall/tdm/model.py

@@ -37,9 +37,9 @@ class Model(ModelBase):
             "tree_parameters.layer_node_num_list", [
                 2, 4, 7, 12], self._namespace)
         self.child_nums = envs.get_global_env(
-            "tree_parameters.node_nums", 2, self._namespace)
-        self.tree_layer_init_path = envs.get_global_env(
-            "tree_parameters.tree_layer_init_path", None, self._namespace)
+            "tree_parameters.child_nums", 2, self._namespace)
+        self.tree_layer_path = envs.get_global_env(
+            "tree.tree_layer_path", None, "train.startup")
 
         # model training hyper parameter
         self.node_emb_size = envs.get_global_env(
@@ -56,7 +56,7 @@ class Model(ModelBase):
         self.topK = envs.get_global_env(
             "hyper_parameters.node_nums", 1, self._namespace)
         self.batch_size = envs.get_global_env(
-            "batch_size", 32, "train.reader")
+            "batch_size", 1, "evaluate.reader")
 
     def train_net(self):
         self.train_input()
@@ -287,16 +287,15 @@ class Model(ModelBase):
             shape=[self.input_emb_size],
             dtype="float32",
         )
-        self._data_var.append(input_emb)
+        self._infer_data_var.append(input_emb)
 
-        if self._platform != "LINUX":
-            self._data_loader = fluid.io.DataLoader.from_generator(
-                feed_list=self._data_var, capacity=64, use_double_buffer=False, iterable=False)
+        self._infer_data_loader = fluid.io.DataLoader.from_generator(
+            feed_list=self._infer_data_var, capacity=64, use_double_buffer=False, iterable=False)
 
     def get_layer_list(self):
         """get layer list from layer_list.txt"""
         layer_list = []
-        with open(self.tree_layer_init_path, 'r') as fin:
+        with open(self.tree_layer_path, 'r') as fin:
             for line in fin.readlines():
                 l = []
                 layer = (line.split('\n'))[0].split(',')
@@ -304,7 +303,7 @@ class Model(ModelBase):
                     if node:
                         l.append(node)
                 layer_list.append(l)
-        return layer_list
+        self.layer_list = layer_list
 
     def create_first_layer(self):
         """decide which layer to start infer"""
@@ -318,16 +317,15 @@ class Model(ModelBase):
         self.first_layer_idx = first_layer_id
         node_list = []
         mask_list = []
-        for id in node_list:
+        for id in first_layer_node:
             node_list.append(fluid.layers.fill_constant(
-                [self.batch_size, 1], value=id, dtype='int64'))
+                [self.batch_size, 1], value=int(id), dtype='int64'))
             mask_list.append(fluid.layers.fill_constant(
                 [self.batch_size, 1], value=0, dtype='int64'))
-
         self.first_layer_node = fluid.layers.concat(node_list, axis=1)
         self.first_layer_node_mask = fluid.layers.concat(mask_list, axis=1)
 
-    def tdm_infer_net(self, inputs):
+    def tdm_infer_net(self):
         """
         infer的主要流程
         infer的基本逻辑是：从上层开始（具体层idx由树结构及TopK值决定）
@@ -336,14 +334,13 @@ class Model(ModelBase):
         3、循环1、2步骤，遍历完所有层，得到每一层筛选结果的集合
         4、将筛选结果集合中的叶子节点，拿出来再做一次topK，得到最终的召回输出
         """
-        input_emb = self._data_var[0]
-
+        input_emb = self._infer_data_var[0]
         node_score = []
         node_list = []
 
         current_layer_node = self.first_layer_node
         current_layer_node_mask = self.first_layer_node_mask
-        input_trans_emb = self.input_trans_net.input_fc_infer(input_emb)
+        input_trans_emb = self.input_fc_infer(input_emb)
 
         for layer_idx in range(self.first_layer_idx, self.max_layers):
             # 确定当前层的需要计算的节点数
@@ -357,10 +354,9 @@ class Model(ModelBase):
                 current_layer_node, [-1, current_layer_node_num])
             current_layer_node_mask = fluid.layers.reshape(
                 current_layer_node_mask, [-1, current_layer_node_num])
-
             node_emb = fluid.embedding(
                 input=current_layer_node,
-                size=[self.node_nums, self.node_embed_size],
+                size=[self.node_nums, self.node_emb_size],
                 param_attr=fluid.ParamAttr(name="TDM_Tree_Emb"))
 
             input_fc_out = self.layer_fc_infer(
@@ -434,6 +430,7 @@ class Model(ModelBase):
         res_item = fluid.layers.slice(
             res_node_emb, axes=[2], starts=[0], ends=[1])
         self.res_item_re = fluid.layers.reshape(res_item, [-1, self.topK])
+        self._infer_results["item"] = self.res_item_re
 
     def input_fc_infer(self, input_emb):
         """

models/recall/tdm/tdm_evaluate_reader.py

+# -*- coding=utf8 -*-
+"""
+#   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.
+"""
+
+from __future__ import print_function
+
+from fleetrec.core.reader import Reader
+
+
+class EvaluateReader(Reader):
+    def init(self):
+        pass
+
+    def generate_sample(self, line):
+        """
+        Read the data line by line and process it as a dictionary
+        """
+        def reader():
+            """
+            This function needs to be implemented by the user, based on data format
+            """
+            features = (line.strip('\n')).split('\t')
+            input_emb = map(float, features[0].split(' '))
+
+            feature_name = ["input_emb"]
+            yield zip(feature_name, [input_emb])
+
+        return reader

models/recall/word2vec/model.py

@@ -25,14 +25,19 @@ class Model(ModelBase):
         ModelBase.__init__(self, config)
 
     def input(self):
-        neg_num = int(envs.get_global_env("hyper_parameters.neg_num", None, self._namespace))
-        self.input_word = fluid.data(name="input_word", shape=[None, 1], dtype='int64')
-        self.true_word = fluid.data(name='true_label', shape=[None, 1], dtype='int64')
+        neg_num = int(envs.get_global_env(
+            "hyper_parameters.neg_num", None, self._namespace))
+        self.input_word = fluid.data(name="input_word", shape=[
+                                     None, 1], dtype='int64')
+        self.true_word = fluid.data(name='true_label', shape=[
+                                    None, 1], dtype='int64')
         self._data_var.append(self.input_word)
         self._data_var.append(self.true_word)
-        with_shuffle_batch = bool(int(envs.get_global_env("hyper_parameters.with_shuffle_batch", None, self._namespace)))
+        with_shuffle_batch = bool(int(envs.get_global_env(
+            "hyper_parameters.with_shuffle_batch", None, self._namespace)))
         if not with_shuffle_batch:
-            self.neg_word = fluid.data(name="neg_label", shape=[None, neg_num], dtype='int64')
+            self.neg_word = fluid.data(name="neg_label", shape=[
+                                       None, neg_num], dtype='int64')
             self._data_var.append(self.neg_word)
 
         if self._platform != "LINUX":
@@ -41,10 +46,14 @@ class Model(ModelBase):
 
     def net(self):
         is_distributed = True if envs.get_trainer() == "CtrTrainer" else False
-        neg_num = int(envs.get_global_env("hyper_parameters.neg_num", None, self._namespace))
-        sparse_feature_number = envs.get_global_env("hyper_parameters.sparse_feature_number", None, self._namespace)
-        sparse_feature_dim = envs.get_global_env("hyper_parameters.sparse_feature_dim", None, self._namespace)
-        with_shuffle_batch = bool(int(envs.get_global_env("hyper_parameters.with_shuffle_batch", None, self._namespace)))
+        neg_num = int(envs.get_global_env(
+            "hyper_parameters.neg_num", None, self._namespace))
+        sparse_feature_number = envs.get_global_env(
+            "hyper_parameters.sparse_feature_number", None, self._namespace)
+        sparse_feature_dim = envs.get_global_env(
+            "hyper_parameters.sparse_feature_dim", None, self._namespace)
+        with_shuffle_batch = bool(int(envs.get_global_env(
+            "hyper_parameters.with_shuffle_batch", None, self._namespace)))
 
         def embedding_layer(input, table_name, emb_dim, initializer_instance=None, squeeze=False):
             emb = fluid.embedding(
@@ -65,28 +74,38 @@ class Model(ModelBase):
         emb_initializer = fluid.initializer.Uniform(-init_width, init_width)
         emb_w_initializer = fluid.initializer.Constant(value=0.0)
 
-        input_emb = embedding_layer(self.input_word, "emb", sparse_feature_dim, emb_initializer, True)
-        true_emb_w = embedding_layer(self.true_word, "emb_w", sparse_feature_dim, emb_w_initializer, True)
-        true_emb_b = embedding_layer(self.true_word, "emb_b", 1, emb_w_initializer, True)
-    
+        input_emb = embedding_layer(
+            self.input_word, "emb", sparse_feature_dim, emb_initializer, True)
+        true_emb_w = embedding_layer(
+            self.true_word, "emb_w", sparse_feature_dim, emb_w_initializer, True)
+        true_emb_b = embedding_layer(
+            self.true_word, "emb_b", 1, emb_w_initializer, True)
+
         if with_shuffle_batch:
             neg_emb_w_list = []
             for i in range(neg_num):
-                neg_emb_w_list.append(fluid.contrib.layers.shuffle_batch(true_emb_w))  # shuffle true_word
-            neg_emb_w_concat = fluid.layers.concat(neg_emb_w_list, axis=0)      
-            neg_emb_w = fluid.layers.reshape(neg_emb_w_concat, shape=[-1, neg_num, sparse_feature_dim])
-        
+                neg_emb_w_list.append(fluid.contrib.layers.shuffle_batch(
+                    true_emb_w))  # shuffle true_word
+            neg_emb_w_concat = fluid.layers.concat(neg_emb_w_list, axis=0)
+            neg_emb_w = fluid.layers.reshape(
+                neg_emb_w_concat, shape=[-1, neg_num, sparse_feature_dim])
+
             neg_emb_b_list = []
             for i in range(neg_num):
-                neg_emb_b_list.append(fluid.contrib.layers.shuffle_batch(true_emb_b))  # shuffle true_word
+                neg_emb_b_list.append(fluid.contrib.layers.shuffle_batch(
+                    true_emb_b))  # shuffle true_word
             neg_emb_b = fluid.layers.concat(neg_emb_b_list, axis=0)
-            neg_emb_b_vec = fluid.layers.reshape(neg_emb_b, shape=[-1, neg_num])
- 
+            neg_emb_b_vec = fluid.layers.reshape(
+                neg_emb_b, shape=[-1, neg_num])
+
         else:
-            neg_emb_w = embedding_layer(self.neg_word, "emb_w", sparse_feature_dim, emb_w_initializer)
-            neg_emb_b = embedding_layer(self.neg_word, "emb_b", 1, emb_w_initializer)
-            neg_emb_b_vec = fluid.layers.reshape(neg_emb_b, shape=[-1, neg_num])
-        
+            neg_emb_w = embedding_layer(
+                self.neg_word, "emb_w", sparse_feature_dim, emb_w_initializer)
+            neg_emb_b = embedding_layer(
+                self.neg_word, "emb_b", 1, emb_w_initializer)
+            neg_emb_b_vec = fluid.layers.reshape(
+                neg_emb_b, shape=[-1, neg_num])
+
         true_logits = fluid.layers.elementwise_add(
             fluid.layers.reduce_sum(
                 fluid.layers.elementwise_mul(input_emb, true_emb_w),
@@ -95,17 +114,18 @@ class Model(ModelBase):
             true_emb_b)
 
         input_emb_re = fluid.layers.reshape(
-	    input_emb, shape=[-1, 1, sparse_feature_dim])
-        neg_matmul = fluid.layers.matmul(input_emb_re, neg_emb_w, transpose_y=True)
+            input_emb, shape=[-1, 1, sparse_feature_dim])
+        neg_matmul = fluid.layers.matmul(
+            input_emb_re, neg_emb_w, transpose_y=True)
         neg_logits = fluid.layers.elementwise_add(
             fluid.layers.reshape(neg_matmul, shape=[-1, neg_num]),
             neg_emb_b_vec)
-        
-	label_ones = fluid.layers.fill_constant_batch_size_like(
+
+        label_ones = fluid.layers.fill_constant_batch_size_like(
             true_logits, shape=[-1, 1], value=1.0, dtype='float32')
         label_zeros = fluid.layers.fill_constant_batch_size_like(
             true_logits, shape=[-1, neg_num], value=0.0, dtype='float32')
-    
+
         true_xent = fluid.layers.sigmoid_cross_entropy_with_logits(true_logits,
                                                                    label_ones)
         neg_xent = fluid.layers.sigmoid_cross_entropy_with_logits(neg_logits,
@@ -116,10 +136,12 @@ class Model(ModelBase):
             fluid.layers.reduce_sum(
                 neg_xent, dim=1))
         self.avg_cost = fluid.layers.reduce_mean(cost)
-        global_right_cnt = fluid.layers.create_global_var(name="global_right_cnt", persistable=True, dtype='float32', shape=[1], value=0)
-        global_total_cnt = fluid.layers.create_global_var(name="global_total_cnt", persistable=True, dtype='float32', shape=[1], value=0)
+        global_right_cnt = fluid.layers.create_global_var(
+            name="global_right_cnt", persistable=True, dtype='float32', shape=[1], value=0)
+        global_total_cnt = fluid.layers.create_global_var(
+            name="global_total_cnt", persistable=True, dtype='float32', shape=[1], value=0)
         global_right_cnt.stop_gradient = True
-        global_total_cnt.stop_gradient = True 
+        global_total_cnt.stop_gradient = True
 
     def avg_loss(self):
         self._cost = self.avg_cost
@@ -134,9 +156,12 @@ class Model(ModelBase):
         self.metrics()
 
     def optimizer(self):
-        learning_rate = envs.get_global_env("hyper_parameters.learning_rate", None, self._namespace)
-        decay_steps = envs.get_global_env("hyper_parameters.decay_steps", None, self._namespace)
-        decay_rate = envs.get_global_env("hyper_parameters.decay_rate", None, self._namespace)
+        learning_rate = envs.get_global_env(
+            "hyper_parameters.learning_rate", None, self._namespace)
+        decay_steps = envs.get_global_env(
+            "hyper_parameters.decay_steps", None, self._namespace)
+        decay_rate = envs.get_global_env(
+            "hyper_parameters.decay_rate", None, self._namespace)
         optimizer = fluid.optimizer.SGD(
             learning_rate=fluid.layers.exponential_decay(
                 learning_rate=learning_rate,
@@ -146,19 +171,27 @@ class Model(ModelBase):
         return optimizer
 
     def analogy_input(self):
-        sparse_feature_number = envs.get_global_env("hyper_parameters.sparse_feature_number", None, self._namespace)
-	self.analogy_a = fluid.data(name="analogy_a", shape=[None], dtype='int64')
-        self.analogy_b = fluid.data(name="analogy_b", shape=[None], dtype='int64')
-        self.analogy_c = fluid.data(name="analogy_c", shape=[None], dtype='int64')
-        self.analogy_d = fluid.data(name="analogy_d", shape=[None], dtype='int64')
-        self._infer_data_var = [self.analogy_a, self.analogy_b, self.analogy_c, self.analogy_d]
+        sparse_feature_number = envs.get_global_env(
+            "hyper_parameters.sparse_feature_number", None, self._namespace)
+        self.analogy_a = fluid.data(
+            name="analogy_a", shape=[None], dtype='int64')
+        self.analogy_b = fluid.data(
+            name="analogy_b", shape=[None], dtype='int64')
+        self.analogy_c = fluid.data(
+            name="analogy_c", shape=[None], dtype='int64')
+        self.analogy_d = fluid.data(
+            name="analogy_d", shape=[None], dtype='int64')
+        self._infer_data_var = [self.analogy_a,
+                                self.analogy_b, self.analogy_c, self.analogy_d]
 
         self._infer_data_loader = fluid.io.DataLoader.from_generator(
             feed_list=self._infer_data_var, capacity=64, use_double_buffer=False, iterable=False)
- 
+
     def infer_net(self):
-        sparse_feature_dim = envs.get_global_env("hyper_parameters.sparse_feature_dim", None, self._namespace)
-        sparse_feature_number = envs.get_global_env("hyper_parameters.sparse_feature_number", None, self._namespace)
+        sparse_feature_dim = envs.get_global_env(
+            "hyper_parameters.sparse_feature_dim", None, self._namespace)
+        sparse_feature_number = envs.get_global_env(
+            "hyper_parameters.sparse_feature_number", None, self._namespace)
 
         def embedding_layer(input, table_name, initializer_instance=None):
             emb = fluid.embedding(
@@ -166,30 +199,36 @@ class Model(ModelBase):
                 size=[sparse_feature_number, sparse_feature_dim],
                 param_attr=table_name)
             return emb
-        
+
         self.analogy_input()
-        all_label = np.arange(sparse_feature_number).reshape(sparse_feature_number).astype('int32')
-        self.all_label = fluid.layers.cast(x=fluid.layers.assign(all_label), dtype='int64')
+        all_label = np.arange(sparse_feature_number).reshape(
+            sparse_feature_number).astype('int32')
+        self.all_label = fluid.layers.cast(
+            x=fluid.layers.assign(all_label), dtype='int64')
         emb_all_label = embedding_layer(self.all_label, "emb")
         emb_a = embedding_layer(self.analogy_a, "emb")
         emb_b = embedding_layer(self.analogy_b, "emb")
         emb_c = embedding_layer(self.analogy_c, "emb")
-        
+
         target = fluid.layers.elementwise_add(
             fluid.layers.elementwise_sub(emb_b, emb_a), emb_c)
 
         emb_all_label_l2 = fluid.layers.l2_normalize(x=emb_all_label, axis=1)
-        dist = fluid.layers.matmul(x=target, y=emb_all_label_l2, transpose_y=True)
+        dist = fluid.layers.matmul(
+            x=target, y=emb_all_label_l2, transpose_y=True)
         values, pred_idx = fluid.layers.topk(input=dist, k=4)
-        label = fluid.layers.expand(fluid.layers.unsqueeze(self.analogy_d, axes=[1]), expand_times=[1, 4])
+        label = fluid.layers.expand(fluid.layers.unsqueeze(
+            self.analogy_d, axes=[1]), expand_times=[1, 4])
         label_ones = fluid.layers.fill_constant_batch_size_like(
             label, shape=[-1, 1], value=1.0, dtype='float32')
         right_cnt = fluid.layers.reduce_sum(
             input=fluid.layers.cast(fluid.layers.equal(pred_idx, label), dtype='float32'))
         total_cnt = fluid.layers.reduce_sum(label_ones)
 
-        global_right_cnt = fluid.layers.create_global_var(name="global_right_cnt", persistable=True, dtype='float32', shape=[1], value=0)
-        global_total_cnt = fluid.layers.create_global_var(name="global_total_cnt", persistable=True, dtype='float32', shape=[1], value=0)
+        global_right_cnt = fluid.layers.create_global_var(
+            name="global_right_cnt", persistable=True, dtype='float32', shape=[1], value=0)
+        global_total_cnt = fluid.layers.create_global_var(
+            name="global_total_cnt", persistable=True, dtype='float32', shape=[1], value=0)
         global_right_cnt.stop_gradient = True
         global_total_cnt.stop_gradient = True
 
@@ -197,6 +236,7 @@ class Model(ModelBase):
         fluid.layers.assign(tmp1, global_right_cnt)
         tmp2 = fluid.layers.elementwise_add(total_cnt, global_total_cnt)
         fluid.layers.assign(tmp2, global_total_cnt)
-        
-        acc = fluid.layers.elementwise_div(global_right_cnt, global_total_cnt, name="total_acc")
+
+        acc = fluid.layers.elementwise_div(
+            global_right_cnt, global_total_cnt, name="total_acc")
         self._infer_results['acc'] = acc