Fix some problems of simnet (#3433)

* update

* update

* Update README.md

* Update run.sh

PaddleNLP/models/matching/bow.py

@@ -49,7 +49,7 @@ class BOW(object):
         right_soft = softsign_layer.ops(right_pool)
         # matching layer
         if self.task_mode == "pairwise":
-            bow_layer = layers.FCLayer(self.bow_dim, "relu", "fc")
+            bow_layer = layers.FCLayer(self.bow_dim, None, "fc")
             left_bow = bow_layer.ops(left_soft)
             right_bow = bow_layer.ops(right_soft)
             cos_sim_layer = layers.CosSimLayer()
@@ -58,7 +58,7 @@ class BOW(object):
         else:
             concat_layer = layers.ConcatLayer(1)
             concat = concat_layer.ops([left_soft, right_soft])
-            bow_layer = layers.FCLayer(self.bow_dim, "relu", "fc")
+            bow_layer = layers.FCLayer(self.bow_dim, None, "fc")
             concat_fc = bow_layer.ops(concat)
             softmax_layer = layers.FCLayer(2, "softmax", "cos_sim")
             pred = softmax_layer.ops(concat_fc)

PaddleNLP/models/matching/cnn.py

@@ -43,23 +43,23 @@ class CNN(object):
         left_emb = emb_layer.ops(left)
         right_emb = emb_layer.ops(right)
         # Presentation context
-        cnn_layer = layers.SequenceConvPoolLayer(self.filter_size,
-                                                 self.num_filters, "conv")
+        cnn_layer = layers.SequenceConvPoolLayer(
+            self.filter_size, self.num_filters, "conv")
         left_cnn = cnn_layer.ops(left_emb)
         right_cnn = cnn_layer.ops(right_emb)
         # matching layer
         if self.task_mode == "pairwise":
-            relu_layer = layers.FCLayer(self.hidden_dim, "relu", "relu")
-            left_relu = relu_layer.ops(left_cnn)
-            right_relu = relu_layer.ops(right_cnn)
+            fc_layer = layers.FCLayer(self.hidden_dim, None, "fc")
+            left_fc = fc_layer.ops(left_cnn)
+            right_fc = fc_layer.ops(right_cnn)
             cos_sim_layer = layers.CosSimLayer()
-            pred = cos_sim_layer.ops(left_relu, right_relu)
-            return left_relu, pred
+            pred = cos_sim_layer.ops(left_fc, right_fc)
+            return left_fc, pred
         else:
             concat_layer = layers.ConcatLayer(1)
             concat = concat_layer.ops([left_cnn, right_cnn])
-            relu_layer = layers.FCLayer(self.hidden_dim, "relu", "relu")
-            concat_fc = relu_layer.ops(concat)
+            fc_layer = layers.FCLayer(self.hidden_dim, None, "fc")
+            concat_fc = fc_layer.ops(concat)
             softmax_layer = layers.FCLayer(2, "softmax", "cos_sim")
             pred = softmax_layer.ops(concat_fc)
             return left_cnn, pred

PaddleNLP/models/matching/gru.py

@@ -50,17 +50,17 @@ class GRU(object):
         right_last = last_layer.ops(right_gru)
         # matching layer
         if self.task_mode == "pairwise":
-            relu_layer = layers.FCLayer(self.hidden_dim, "relu", "relu")
-            left_relu = relu_layer.ops(left_last)
-            right_relu = relu_layer.ops(right_last)
+            fc_layer = layers.FCLayer(self.hidden_dim, None, "fc")
+            left_fc = fc_layer.ops(left_last)
+            right_fc = fc_layer.ops(right_last)
             cos_sim_layer = layers.CosSimLayer()
-            pred = cos_sim_layer.ops(left_relu, right_relu)
-            return left_relu, pred
+            pred = cos_sim_layer.ops(left_fc, right_fc)
+            return left_fc, pred
         else:
             concat_layer = layers.ConcatLayer(1)
             concat = concat_layer.ops([left_last, right_last])
-            relu_layer = layers.FCLayer(self.hidden_dim, "relu", "relu")
-            concat_fc = relu_layer.ops(concat)
+            fc_layer = layers.FCLayer(self.hidden_dim, None, "fc")
+            concat_fc = fc_layer.ops(concat)
             softmax_layer = layers.FCLayer(2, "softmax", "cos_sim")
             pred = softmax_layer.ops(concat_fc)
             return left_last, pred

PaddleNLP/models/matching/lstm.py

@@ -49,17 +49,17 @@ class LSTM(object):
         right_last = last_layer.ops(right_lstm)
         # matching layer
         if self.task_mode == "pairwise":
-            relu_layer = layers.FCLayer(self.hidden_dim, "relu", "relu")
-            left_relu = relu_layer.ops(left_last)
-            right_relu = relu_layer.ops(right_last)
+            fc_layer = layers.FCLayer(self.hidden_dim, None, "fc")
+            left_fc = fc_layer.ops(left_last)
+            right_fc = fc_layer.ops(right_last)
             cos_sim_layer = layers.CosSimLayer()
-            pred = cos_sim_layer.ops(left_relu, right_relu)
-            return left_relu, pred
+            pred = cos_sim_layer.ops(left_fc, right_fc)
+            return left_fc, pred
         else:
             concat_layer = layers.ConcatLayer(1)
             concat = concat_layer.ops([left_last, right_last])
-            relu_layer = layers.FCLayer(self.hidden_dim, "relu", "relu")
-            concat_fc = relu_layer.ops(concat)
+            fc_layer = layers.FCLayer(self.hidden_dim, None, "fc")
+            concat_fc = fc_layer.ops(concat)
             softmax_layer = layers.FCLayer(2, "softmax", "cos_sim")
             pred = softmax_layer.ops(concat_fc)
             return left_last, pred

PaddleNLP/similarity_net/README.md

@@ -6,10 +6,17 @@
 基于百度海量搜索数据，我们训练了一个SimNet-BOW-Pairwise语义匹配模型，在一些真实的FAQ问答场景中，该模型效果比基于字面的相似度方法AUC提升5%以上，我们基于百度自建测试集（包含聊天、客服等数据集）和语义匹配数据集(LCQMC)进行评测，效果如下表所示。LCQMC数据集以Accuracy为评测指标，而pairwise模型的输出为相似度，因此我们采用0.958作为分类阈值，相比于基线模型中网络结构同等复杂的CBOW模型（准确率为0.737），我们模型的准确率为0.7532。
 
 
-| 模型       | 百度知道         | ECOM        |QQSIM | UNICOM | LCQMC |
-|:-----------:|:-------------:|:-------------:|:-------------:|:-------------:|:-------------:|
-|   | AUC  | AUC | AUC|正逆序比|Accuracy|
-|BOW_Pairwise|0.6767|0.7329|0.7650|1.5630|0.7532|
+| 模型       | 百度知道         | ECOM        |QQSIM | UNICOM | 
+|:-----------:|:-------------:|:-------------:|:-------------:|:-------------:|
+|   | AUC  | AUC | AUC|正逆序比|
+|BOW_Pairwise|0.6767|0.7329|0.7650|1.5630|
+#### 测试集说明
+| 数据集       | 来源         | 垂类        |
+|:-----------:|:-------------:|:-------------:|
+|百度知道   | 百度知道问题  | 日常 |
+|ECOM|商业问句|金融|
+|QQSIM|闲聊对话|日常|
+|UNICOM|联通客服|客服|
 ## 快速开始
 #### 版本依赖
 本项目依赖于 Paddlepaddle Fluid 1.3.1，请参考[安装指南](http://www.paddlepaddle.org/#quick-start)进行安装。
@@ -24,24 +31,14 @@ cd models/PaddleNLP/similarity_net
 #### 数据准备
 下载经过预处理的数据，运行命令后，data目录下会存在训练集数据示例、集数据示例、测试集数据示例，以及对应词索引字典（term2id.dict）。
 ```shell
-wget --no-check-certificate https://baidu-nlp.bj.bcebos.com/simnet_dataset-1.0.0.tar.gz
-tar xzf simnet_dataset-1.0.0.tar.gz
+sh download_data.sh
 ```
 #### 模型准备
-我们开源了基于大规模数据训练好的```pairwise```模型（基于bow模型训练），我们提供两种下载方式，模型保在```./model_files/simnet_bow_pairwise_pretrained_model/```下。
-
-##### 方式一：基于PaddleHub命令行工具（PaddleHub[安装方式](https://github.com/PaddlePaddle/PaddleHub)）
-```shell
-mkdir model_files
-hub download simnet_bow_pairwise --output_path ./
-tar xzf simnet_bow-pairwise-1.0.0.tar.gz -C ./model_files
-```
-##### 方式二：直接下载
+我们开源了基于大规模数据训练好的```pairwise```模型（基于bow模型训练），用户可以通过运行命令下载预训练好的模型，该模型将保存在```./model_files/simnet_bow_pairwise_pretrained_model/```下。
 ```shell
-mkdir model_files
-wget --no-check-certificate https://baidu-nlp.bj.bcebos.com/simnet_bow-pairwise-1.0.0.tar.gz
-tar xzf simnet_bow-pairwise-1.0.0.tar.gz -C ./model_files
+sh download_pretrained_model.sh
 ```
+
 #### 评估
 我们公开了自建的测试集，包括百度知道、ECOM、QQSIM、UNICOM四个数据集，基于上面的预训练模型，用户可以进入evaluate目录下依次执行下列命令获取测试集评估结果。
 ```shell
@@ -162,6 +159,7 @@ python run_classifier.py \
    --task_mode ${TASK_MODE} #训练模式，pairwise或pointwise，与相应的配置文件匹配。
    --compute_accuracy False \   #是否计算accuracy
    --lamda 0.91 \    #pairwise模式计算accuracy时的阈值
+   --init_checkpoint "" #预加载模型路径
 ```
 ### 如何组建自己的模型
 用户可以根据自己的需求，组建自定义的模型，具体方法如下所示：

PaddleNLP/similarity_net/config.py

@@ -34,14 +34,12 @@ class SimNetConfig(object):
             with open(config_path) as json_file:
                 config_dict = json.load(json_file)
         except Exception:
-            raise IOError("Error in parsing simnet model config file '%s'" %
-                          config_path)
+            raise IOError("Error in parsing simnet model config file '%s'" % config_path)
 
         else:
             if config_dict["task_mode"] != self.task_mode:
                 raise ValueError(
-                    "the config '{}' does not match the task_mode '{}'".format(
-                        self.config_path, self.task_mode))
+                    "the config '{}' does not match the task_mode '{}'".format(self.config_path, self.task_mode))
             return config_dict
 
     def __getitem__(self, key):

PaddleNLP/similarity_net/download_data.sh

+#get data
+wget --no-check-certificate https://baidu-nlp.bj.bcebos.com/simnet_dataset-1.0.0.tar.gz
+tar xzf simnet_dataset-1.0.0.tar.gz
+rm simnet_dataset-1.0.0.tar.gz
+

PaddleNLP/similarity_net/download.sh → PaddleNLP/similarity_net/download_pretrained_model.sh

@@ -8,9 +8,3 @@ if [ ! -d $model_files_path ]; then
 fi
 tar xzf simnet_bow-pairwise-1.0.0.tar.gz -C $model_files_path
 rm simnet_bow-pairwise-1.0.0.tar.gz
-
-#get data
-wget --no-check-certificate https://baidu-nlp.bj.bcebos.com/simnet_dataset-1.0.0.tar.gz
-tar xzf simnet_dataset-1.0.0.tar.gz
-rm simnet_dataset-1.0.0.tar.gz
-

PaddleNLP/similarity_net/run.sh

@@ -21,7 +21,7 @@ INIT_CHECKPOINT=./model_files/simnet_bow_pairwise_pretrained_model/
 train() {
 	python run_classifier.py \
 		--task_name ${TASK_NAME} \
-		--use_cuda false \
+		--use_cuda False \
 		--do_train True \
 		--do_valid True \
 		--do_test True \
@@ -34,12 +34,13 @@ train() {
 		--output_dir ${CKPT_PATH} \
 		--config_path ${CONFIG_PATH} \
 		--vocab_path ${VOCAB_PATH} \
-		--epoch 10 \
-		--save_steps 1000 \
-		--validation_steps 100 \
+		--epoch 40 \
+		--save_steps 2000 \
+		--validation_steps 200 \
 		--compute_accuracy False \
 		--lamda 0.958 \
-		--task_mode ${TASK_MODE}
+		--task_mode ${TASK_MODE}\
+		--init_checkpoint ""
 }
 #run_evaluate
 evaluate() {

PaddleNLP/similarity_net/run_classifier.py

@@ -25,75 +25,66 @@ import argparse
 import multiprocessing
 import sys
 
+defaultencoding = 'utf-8'
+if sys.getdefaultencoding() != defaultencoding:
+    reload(sys)
+    sys.setdefaultencoding(defaultencoding)
+
 sys.path.append("..")
 
 import paddle
 import paddle.fluid as fluid
 import numpy as np
-import codecs
 import config
 import utils
 import reader
 import models.matching.paddle_layers as layers
+import codecs
+
+from utils import ArgConfig
 
 import logging
 
-parser = argparse.ArgumentParser(__doc__)
-model_g = utils.ArgumentGroup(parser, "model", "model configuration and paths.")
-model_g.add_arg("config_path", str, None,
-                "Path to the json file for EmoTect model config.")
-model_g.add_arg("init_checkpoint", str, None,
-                "Init checkpoint to resume training from.")
-model_g.add_arg("output_dir", str, None, "Directory path to save checkpoints")
-model_g.add_arg("task_mode", str, None, "task mode: pairwise or pointwise")
-
-train_g = utils.ArgumentGroup(parser, "training", "training options.")
-train_g.add_arg("epoch", int, 10, "Number of epoches for training.")
-train_g.add_arg("save_steps", int, 200,
-                "The steps interval to save checkpoints.")
-train_g.add_arg("validation_steps", int, 100,
-                "The steps interval to evaluate model performance.")
-
-log_g = utils.ArgumentGroup(parser, "logging", "logging related")
-log_g.add_arg("skip_steps", int, 10, "The steps interval to print loss.")
-log_g.add_arg("verbose_result", bool, True, "Whether to output verbose result.")
-log_g.add_arg("test_result_path", str, "test_result",
-              "Directory path to test result.")
-log_g.add_arg("infer_result_path", str, "infer_result",
-              "Directory path to infer result.")
-
-data_g = utils.ArgumentGroup(
-    parser, "data", "Data paths, vocab paths and data processing options")
-data_g.add_arg("train_data_dir", str, None, "Directory path to training data.")
-data_g.add_arg("valid_data_dir", str, None, "Directory path to valid data.")
-data_g.add_arg("test_data_dir", str, None, "Directory path to testing data.")
-data_g.add_arg("infer_data_dir", str, None, "Directory path to infer data.")
-data_g.add_arg("vocab_path", str, None, "Vocabulary path.")
-data_g.add_arg("batch_size", int, 32,
-               "Total examples' number in batch for training.")
-
-run_type_g = utils.ArgumentGroup(parser, "run_type", "running type options.")
-run_type_g.add_arg("use_cuda", bool, False, "If set, use GPU for training.")
-run_type_g.add_arg("task_name", str, None,
-                   "The name of task to perform sentiment classification.")
-run_type_g.add_arg("do_train", bool, False, "Whether to perform training.")
-run_type_g.add_arg("do_valid", bool, False, "Whether to perform dev.")
-run_type_g.add_arg("do_test", bool, False, "Whether to perform testing.")
-run_type_g.add_arg("do_infer", bool, False, "Whether to perform inference.")
-run_type_g.add_arg("compute_accuracy", bool, False,
-                   "Whether to compute accuracy.")
-run_type_g.add_arg(
-    "lamda", float, 0.91,
-    "When task_mode is pairwise, lamda is the threshold for calculating the accuracy."
-)
-
-parser.add_argument(
-    '--enable_ce',
-    action='store_true',
-    help='If set, run the task with continuous evaluation logs.')
-
-args = parser.parse_args()
+def create_model(args, pyreader_name, is_inference = False, is_pointwise = False):
+    """
+    Create Model for simnet
+    """
+    if is_inference:
+        inf_pyreader = fluid.layers.py_reader(
+        capacity=16,
+        shapes=([-1,1], [-1,1]),
+        dtypes=('int64', 'int64'),
+        lod_levels=(1, 1),
+        name=pyreader_name,
+        use_double_buffer=False)
+
+        left, pos_right = fluid.layers.read_file(inf_pyreader)
+        return inf_pyreader, left, pos_right
+
+    else:
+        if is_pointwise:
+            pointwise_pyreader = fluid.layers.py_reader(
+            capacity=16,
+            shapes=([-1,1], [-1,1], [-1,1]),
+            dtypes=('int64', 'int64', 'int64'),
+            lod_levels=(1, 1, 0),
+            name=pyreader_name,
+            use_double_buffer=False)
+
+            left, right, label = fluid.layers.read_file(pointwise_pyreader)
+            return pointwise_pyreader, left, right, label
 
+        else:
+            pairwise_pyreader = fluid.layers.py_reader(
+            capacity=16,
+            shapes=([-1,1], [-1,1], [-1,1]),
+            dtypes=('int64', 'int64', 'int64'),
+            lod_levels=(1, 1, 1),
+            name=pyreader_name,
+            use_double_buffer=False)
+
+            left, pos_right, neg_right = fluid.layers.read_file(pairwise_pyreader)
+            return pairwise_pyreader, left, pos_right, neg_right
         
 def train(conf_dict, args):
     """
@@ -129,85 +120,79 @@ def train(conf_dict, args):
         place = fluid.CUDAPlace(0)
     else:
         place = fluid.CPUPlace()
+    exe = fluid.Executor(place)
+    startup_prog = fluid.Program()
+    train_program = fluid.Program()
 
     simnet_process = reader.SimNetProcessor(args, vocab)
     if args.task_mode == "pairwise":
         # Build network
-        left = data.ops(name="left", shape=[1], dtype="int64", lod_level=1)
-        pos_right = data.ops(name="right",
-                             shape=[1],
-                             dtype="int64",
-                             lod_level=1)
-        neg_right = data.ops(name="neg_right",
-                             shape=[1],
-                             dtype="int64",
-                             lod_level=1)
+        with fluid.program_guard(train_program, startup_prog):
+            with fluid.unique_name.guard():
+                train_pyreader, left, pos_right, neg_right = create_model(
+                    args, 
+                    pyreader_name='train_reader')
                 left_feat, pos_score = net.predict(left, pos_right)
-
-        # Get Feeder and Reader
-        train_feeder = fluid.DataFeeder(
-            place=place, feed_list=[left.name, pos_right.name, neg_right.name])
-        train_reader = simnet_process.get_reader("train")
-        if args.do_valid:
-            valid_feeder = fluid.DataFeeder(
-                place=place, feed_list=[left.name, pos_right.name])
-            valid_reader = simnet_process.get_reader("valid")
                 pred = pos_score
-        # Save Infer model
-        infer_program = fluid.default_main_program().clone(for_test=True)
                 _, neg_score = net.predict(left, neg_right)
                 avg_cost = loss.compute(pos_score, neg_score)
                 avg_cost.persistable = True
+                optimizer.ops(avg_cost)
+                
+        # Get Reader
+        get_train_examples = simnet_process.get_reader("train")
+        if args.do_valid:
+            test_prog = fluid.Program()
+            with fluid.program_guard(test_prog, startup_prog):
+                with fluid.unique_name.guard():
+                    test_pyreader, left, pos_right= create_model(args, pyreader_name = 'test_reader',is_inference=True)
+                    left_feat, pos_score = net.predict(left, pos_right)
+                    pred = pos_score
+            test_prog = test_prog.clone(for_test=True)
+
     else:
         # Build network
-        left = data.ops(name="left", shape=[1], dtype="int64", lod_level=1)
-        right = data.ops(name="right", shape=[1], dtype="int64", lod_level=1)
-        label = data.ops(name="label", shape=[1], dtype="int64", lod_level=0)
+        with fluid.program_guard(train_program, startup_prog):
+            with fluid.unique_name.guard():
+                train_pyreader, left, right, label = create_model(
+                    args, 
+                    pyreader_name='train_reader',
+                    is_pointwise=True)
                 left_feat, pred = net.predict(left, right)
+                avg_cost = loss.compute(pred, label)
+                avg_cost.persistable = True
+                optimizer.ops(avg_cost)
 
         # Get Feeder and Reader
-        train_feeder = fluid.DataFeeder(
-            place=place, feed_list=[left.name, right.name, label.name])
-        train_reader = simnet_process.get_reader("train")
+        get_train_examples = simnet_process.get_reader("train")
         if args.do_valid:
-            valid_feeder = fluid.DataFeeder(
-                place=place, feed_list=[left.name, right.name])
-            valid_reader = simnet_process.get_reader("valid")
-        # Save Infer model
-        infer_program = fluid.default_main_program().clone(for_test=True)
-        avg_cost = loss.compute(pred, label)
-        avg_cost.persistable = True
+            test_prog = fluid.Program()
+            with fluid.program_guard(test_prog, startup_prog):
+                with fluid.unique_name.guard():
+                    test_pyreader, left, right= create_model(args, pyreader_name = 'test_reader',is_inference=True)
+                    left_feat, pred = net.predict(left, right)
+            test_prog = test_prog.clone(for_test=True)
 
-    # operate Optimization
-    optimizer.ops(avg_cost)
-    executor = fluid.Executor(place)
-    executor.run(fluid.default_startup_program())
-
-    if args.init_checkpoint is not None:
-        utils.init_checkpoint(executor, args.init_checkpoint,
-                              fluid.default_startup_program())
-
-    # Get and run executor
-    parallel_executor = fluid.ParallelExecutor(
-        use_cuda=args.use_cuda,
-        loss_name=avg_cost.name,
-        main_program=fluid.default_main_program())
-    # Get device number
-    device_count = parallel_executor.device_count
-    logging.info("device count: %d" % device_count)
-
-    def valid_and_test(program, feeder, reader, process, mode="test"):
+    if args.init_checkpoint is not "":
+        utils.init_checkpoint(exe, args.init_checkpoint, 
+                              startup_prog)
+
+    def valid_and_test(test_program, test_pyreader, get_valid_examples, process, mode, exe, fetch_list):
         """
         return auc and acc
         """
         # Get Batch Data
-        batch_data = paddle.batch(reader, args.batch_size, drop_last=False)
+        batch_data = paddle.batch(get_valid_examples, args.batch_size, drop_last=False)
+        test_pyreader.decorate_paddle_reader(batch_data)
+        test_pyreader.start()
         pred_list = []
-        for data in batch_data():
-            _pred = executor.run(program=program,
-                                 feed=feeder.feed(data),
-                                 fetch_list=[pred.name])
+        while True:
+            try:
+                _pred = exe.run(program=test_program,fetch_list=[pred.name])
                 pred_list += list(_pred)
+            except fluid.core.EOFException:
+                test_pyreader.reset()
+                break
         pred_list = np.vstack(pred_list)
         if mode == "test":
             label_list = process.get_test_label()
@@ -232,32 +217,26 @@ def train(conf_dict, args):
     # set global step
     global_step = 0
     ce_info = []
+    train_exe = exe
     for epoch_id in range(args.epoch):
-        losses = []
-        # Get batch data iterator
         train_batch_data = paddle.batch(
             paddle.reader.shuffle(
-                train_reader, buf_size=10000),
+                get_train_examples, buf_size=10000),
             args.batch_size,
             drop_last=False)
+        train_pyreader.decorate_paddle_reader(train_batch_data)
+        train_pyreader.start()
+        exe.run(startup_prog)
+        losses = []
         start_time = time.time()
-        for iter, data in enumerate(train_batch_data()):
-            if len(data) < device_count:
-                logging.info(
-                    "the size of batch data is less than device_count(%d)" %
-                    device_count)
-                continue
+        while True:
+            try:
                 global_step += 1
-            avg_loss = parallel_executor.run([avg_cost.name],
-                                             feed=train_feeder.feed(data))
+                fetch_list = [avg_cost.name]
+                avg_loss = train_exe.run(program=train_program, fetch_list = fetch_list)
                 if args.do_valid and global_step % args.validation_steps == 0:
-
-                valid_result = valid_and_test(
-                    program=infer_program,
-                    feeder=valid_feeder,
-                    reader=valid_reader,
-                    process=simnet_process,
-                    mode="valid")
+                    get_valid_examples = simnet_process.get_reader("valid")
+                    valid_result = valid_and_test(test_prog,test_pyreader,get_valid_examples,simnet_process,"valid",exe,[pred.name])
                     if args.compute_accuracy:
                         valid_auc, valid_acc = valid_result
                         logging.info(
@@ -284,14 +263,39 @@ def train(conf_dict, args):
                         ]
                         target_vars = [left_feat, pred]
                     fluid.io.save_inference_model(model_path, feed_var_names,
-                                              target_vars, executor,
-                                              infer_program)
+                                                  target_vars, exe,
+                                                  test_prog)
                     logging.info("saving infer model in %s" % model_path)
                 losses.append(np.mean(avg_loss[0]))
+            
+            except fluid.core.EOFException:
+                train_pyreader.reset()
+                break
         end_time = time.time()
         logging.info("epoch: %d, loss: %f, used time: %d sec" %
                      (epoch_id, np.mean(losses), end_time - start_time))
         ce_info.append([np.mean(losses), end_time - start_time])
+    #final save
+    logging.info("the final step is %s" % global_step)    
+    model_save_dir = os.path.join(args.output_dir,
+                                conf_dict["model_path"])
+    model_path = os.path.join(model_save_dir, str(global_step))
+    if not os.path.exists(model_save_dir):
+        os.makedirs(model_save_dir)
+    if args.task_mode == "pairwise":
+        feed_var_names = [left.name, pos_right.name]
+        target_vars = [left_feat, pos_score]
+    else:
+        feed_var_names = [
+            left.name,
+            right.name,
+        ]
+        target_vars = [left_feat, pred]
+    fluid.io.save_inference_model(model_path, feed_var_names,
+                                target_vars, exe,
+                                test_prog)
+    logging.info("saving infer model in %s" % model_path)
+
     if args.enable_ce:
         card_num = get_cards()
         ce_loss = 0
@@ -309,20 +313,11 @@ def train(conf_dict, args):
     if args.do_test:
         if args.task_mode == "pairwise":
             # Get Feeder and Reader
-            test_feeder = fluid.DataFeeder(
-                place=place, feed_list=[left.name, pos_right.name])
-            test_reader = simnet_process.get_reader("test")
+            get_test_examples = simnet_process.get_reader("test")
         else:
             # Get Feeder and Reader
-            test_feeder = fluid.DataFeeder(
-                place=place, feed_list=[left.name, right.name])
-            test_reader = simnet_process.get_reader("test")
-        test_result = valid_and_test(
-            program=infer_program,
-            feeder=test_feeder,
-            reader=test_reader,
-            process=simnet_process,
-            mode="test")
+            get_test_examples = simnet_process.get_reader("test")
+        test_result = valid_and_test(test_prog,test_pyreader,get_test_examples,simnet_process,"test",exe,[pred.name])
         if args.compute_accuracy:
             test_auc, test_acc = test_result
             logging.info("AUC of test is %f, Accuracy of test is %f" %
@@ -334,51 +329,83 @@ def train(conf_dict, args):
 
 def test(conf_dict, args):
     """
-    run predict
+    Evaluation Function
     """
-    vocab = utils.load_vocab(args.vocab_path)
-    simnet_process = reader.SimNetProcessor(args, vocab)
-    # load auc method
-    metric = fluid.metrics.Auc(name="auc")
-    with codecs.open("predictions.txt", "w", "utf-8") as predictions_file:
-        # Get model path
-        model_path = args.init_checkpoint
-        # Get device
     if args.use_cuda:
         place = fluid.CUDAPlace(0)
     else:
         place = fluid.CPUPlace()
-        # Get executor
-        executor = fluid.Executor(place=place)
-        # Load model
-        program, feed_var_names, fetch_targets = fluid.io.load_inference_model(
-            model_path, executor)
+    exe = fluid.Executor(place)
+
+    vocab = utils.load_vocab(args.vocab_path)
+    simnet_process = reader.SimNetProcessor(args, vocab)
+    
+    startup_prog = fluid.Program()
+
+    get_test_examples = simnet_process.get_reader("test")
+    batch_data = paddle.batch(get_test_examples, args.batch_size, drop_last=False)
+
+    test_prog = fluid.Program()
+
+    conf_dict['dict_size'] = len(vocab)
+
+    net = utils.import_class("../models/matching",
+                             conf_dict["net"]["module_name"],
+                             conf_dict["net"]["class_name"])(conf_dict)
+
+    metric = fluid.metrics.Auc(name="auc")
+
+    with codecs.open("predictions.txt", "w", "utf-8") as predictions_file:
         if args.task_mode == "pairwise":
-            # Get Feeder and Reader
-            feeder = fluid.DataFeeder(
-                place=place, feed_list=feed_var_names, program=program)
-            test_reader = simnet_process.get_reader("test")
+            with fluid.program_guard(test_prog, startup_prog):
+                with fluid.unique_name.guard():
+                    test_pyreader, left, pos_right = create_model(
+                        args,
+                        pyreader_name = 'test_reader',
+                        is_inference=True)
+                    left_feat, pos_score = net.predict(left, pos_right)
+                    pred = pos_score
+            test_prog = test_prog.clone(for_test=True)
+
         else:
-            # Get Feeder and Reader
-            feeder = fluid.DataFeeder(
-                place=place, feed_list=feed_var_names, program=program)
-            test_reader = simnet_process.get_reader("test")
-        # Get batch data iterator
-        batch_data = paddle.batch(test_reader, args.batch_size, drop_last=False)
+            with fluid.program_guard(test_prog, startup_prog):
+                with fluid.unique_name.guard():
+                    test_pyreader, left, right = create_model(
+                        args,
+                        pyreader_name = 'test_reader',
+                        is_inference=True)
+                    left_feat, pred = net.predict(left, right)
+            test_prog = test_prog.clone(for_test=True)
+
+        exe.run(startup_prog)
+
+        utils.init_checkpoint(
+            exe,
+            args.init_checkpoint,
+            main_program=test_prog)
+        
+        test_exe = exe
+        test_pyreader.decorate_paddle_reader(batch_data)
+
         logging.info("start test process ...")
+        test_pyreader.start()
         pred_list = []
-        for iter, data in enumerate(batch_data()):
-            output = executor.run(program,
-                                  feed=feeder.feed(data),
-                                  fetch_list=fetch_targets)
+        fetch_list = [pred.name]
+        output = []
+        while True:
+            try:
+                output = test_exe.run(program=test_prog,fetch_list=fetch_list)
                 if args.task_mode == "pairwise":
-                pred_list += list(map(lambda item: float(item[0]), output[1]))
+                    pred_list += list(map(lambda item: float(item[0]), output[0]))
                     predictions_file.write("\n".join(
-                    map(lambda item: str((item[0] + 1) / 2), output[1])) + "\n")
+                        map(lambda item: str((item[0] + 1) / 2), output[0])) + "\n")
                 else:
-                pred_list += map(lambda item: item, output[1])
+                    pred_list += map(lambda item: item, output[0])
                     predictions_file.write("\n".join(
-                    map(lambda item: str(np.argmax(item)), output[1])) + "\n")
+                        map(lambda item: str(np.argmax(item)), output[0])) + "\n")
+            except fluid.core.EOFException:
+                test_pyreader.reset()
+                break
         if args.task_mode == "pairwise":
             pred_list = np.array(pred_list).reshape((-1, 1))
             pred_list = (pred_list + 1) / 2
@@ -403,47 +430,72 @@ def test(conf_dict, args):
                      os.path.join(os.getcwd(), args.test_result_path))
 
 
-def infer(args):
+def infer(conf_dict, args):
     """
     run predict
     """
-    vocab = utils.load_vocab(args.vocab_path)
-    simnet_process = reader.SimNetProcessor(args, vocab)
-    # Get model path
-    model_path = args.init_checkpoint
-    # Get device
     if args.use_cuda:
         place = fluid.CUDAPlace(0)
     else:
         place = fluid.CPUPlace()
-    # Get executor
-    executor = fluid.Executor(place=place)
-    # Load model
-    program, feed_var_names, fetch_targets = fluid.io.load_inference_model(
-        model_path, executor)
+    exe = fluid.Executor(place)
+
+    vocab = utils.load_vocab(args.vocab_path)
+    simnet_process = reader.SimNetProcessor(args, vocab)
+
+    startup_prog = fluid.Program()
+
+    get_infer_examples = simnet_process.get_infer_reader
+    batch_data = paddle.batch(get_infer_examples, args.batch_size, drop_last=False)
+
+    test_prog = fluid.Program()
+
+    conf_dict['dict_size'] = len(vocab)
+
+    net = utils.import_class("../models/matching",
+                             conf_dict["net"]["module_name"],
+                             conf_dict["net"]["class_name"])(conf_dict)
+
     if args.task_mode == "pairwise":
-        # Get Feeder and Reader
-        infer_feeder = fluid.DataFeeder(
-            place=place, feed_list=feed_var_names, program=program)
-        infer_reader = simnet_process.get_infer_reader
+        with fluid.program_guard(test_prog, startup_prog):
+            with fluid.unique_name.guard():
+                infer_pyreader, left, pos_right = create_model(args, pyreader_name = 'infer_reader', is_inference = True)
+                left_feat, pos_score = net.predict(left, pos_right)
+                pred = pos_score
+        test_prog = test_prog.clone(for_test=True)
     else:
-        # Get Feeder and Reader
-        infer_feeder = fluid.DataFeeder(
-            place=place, feed_list=feed_var_names, program=program)
-        infer_reader = simnet_process.get_infer_reader
-    # Get batch data iterator
-    batch_data = paddle.batch(infer_reader, args.batch_size, drop_last=False)
+        with fluid.program_guard(test_prog, startup_prog):
+            with fluid.unique_name.guard():
+                infer_pyreader, left, right = create_model(args, pyreader_name = 'infer_reader', is_inference = True)
+                left_feat, pred = net.predict(left, right)
+        test_prog = test_prog.clone(for_test=True)
+
+    exe.run(startup_prog)
+
+    utils.init_checkpoint(
+        exe,
+        args.init_checkpoint,
+        main_program=test_prog)
+    
+    test_exe = exe
+    infer_pyreader.decorate_sample_list_generator(batch_data)
+
     logging.info("start test process ...")
     preds_list = []
-    for iter, data in enumerate(batch_data()):
-        output = executor.run(program,
-                              feed=infer_feeder.feed(data),
-                              fetch_list=fetch_targets)
+    fetch_list = [pred.name]
+    output = []
+    infer_pyreader.start()
+    while True:
+            try:
+                output = test_exe.run(program=test_prog,fetch_list=fetch_list)
                 if args.task_mode == "pairwise":
                     preds_list += list(
-                map(lambda item: str((item[0] + 1) / 2), output[1]))
+                        map(lambda item: str((item[0] + 1) / 2), output[0]))
                 else:
-            preds_list += map(lambda item: str(np.argmax(item)), output[1])
+                    preds_list += map(lambda item: str(np.argmax(item)), output[0])
+            except fluid.core.EOFException:
+                infer_pyreader.reset()
+                break
     with codecs.open(args.infer_result_path, "w", "utf-8") as infer_file:
         for _data, _pred in zip(simnet_process.get_infer_data(), preds_list):
             infer_file.write(_data + "\t" + _pred + "\n")
@@ -458,23 +510,11 @@ def get_cards():
         num = len(cards.split(","))
     return num
 
+if __name__ == "__main__":
 
-def main(conf_dict, args):
-    """
-    main
-    """
-    if args.do_train:
-        train(conf_dict, args)
-    elif args.do_test:
-        test(conf_dict, args)
-    elif args.do_infer:
-        infer(args)
-    else:
-        raise ValueError(
-            "one of do_train and do_test and do_infer must be True")
-
+    args = ArgConfig()
+    args = args.build_conf()
 
-if __name__ == "__main__":
     utils.print_arguments(args)
     try:
         if fluid.is_compiled_with_cuda() != True and args.use_cuda == True:
@@ -487,4 +527,12 @@ if __name__ == "__main__":
         pass
     utils.init_log("./log/TextSimilarityNet")
     conf_dict = config.SimNetConfig(args)
-    main(conf_dict, args)
+    if args.do_train:
+        train(conf_dict, args)
+    elif args.do_test:
+        test(conf_dict, args)
+    elif args.do_infer:
+        infer(conf_dict, args)
+    else:
+        raise ValueError(
+            "one of do_train and do_test and do_infer must be True")
\ No newline at end of file

PaddleNLP/similarity_net/utils.py

@@ -15,7 +15,7 @@
 """
 SimNet utilities.
 """
-
+import argparse
 import time
 import sys
 import re
@@ -26,20 +26,17 @@ import numpy as np
 import logging
 import logging.handlers
 import paddle.fluid as fluid
+import io
 """
 ******functions for file processing******
 """
 
-
 def load_vocab(file_path):
     """
     load the given vocabulary
     """
     vocab = {}
-    if six.PY3:
-        f = open(file_path, "r", encoding="utf-8")
-    else:
-        f = open(file_path, "r")
+    f = io.open(file_path, "r", encoding="utf-8")
     for line in f:
         items = line.strip("\n").split("\t")
         if items[0] not in vocab:
@@ -61,8 +58,7 @@ def get_result_file(args):
     """
     with codecs.open(args.test_data_dir, "r", "utf-8") as test_file:
         with codecs.open("predictions.txt", "r", "utf-8") as predictions_file:
-            with codecs.open(args.test_result_path, "w",
-                             "utf-8") as test_result_file:
+            with codecs.open(args.test_result_path, "w", "utf-8") as test_result_file:
                 test_datas = [line.strip("\n") for line in test_file]
                 predictions = [line.strip("\n") for line in predictions_file]
                 for test_data, prediction in zip(test_datas, predictions):
@@ -170,6 +166,58 @@ class ArgumentGroup(object):
             help=help + ' Default: %(default)s.',
             **kwargs)
 
+class ArgConfig(object):
+    def __init__(self):
+        parser = argparse.ArgumentParser()
+
+        model_g = ArgumentGroup(parser, "model", "model configuration and paths.")
+        model_g.add_arg("config_path", str, None, "Path to the json file for EmoTect model config.")
+        model_g.add_arg("init_checkpoint", str, None, "Init checkpoint to resume training from.")
+        model_g.add_arg("output_dir", str, None, "Directory path to save checkpoints")
+        model_g.add_arg("task_mode", str, None, "task mode: pairwise or pointwise")
+
+        train_g = ArgumentGroup(parser, "training", "training options.")
+        train_g.add_arg("epoch", int, 10, "Number of epoches for training.")
+        train_g.add_arg("save_steps", int, 200, "The steps interval to save checkpoints.")
+        train_g.add_arg("validation_steps", int, 100, "The steps interval to evaluate model performance.")
+
+        log_g = ArgumentGroup(parser, "logging", "logging related")
+        log_g.add_arg("skip_steps", int, 10, "The steps interval to print loss.")
+        log_g.add_arg("verbose_result", bool, True, "Whether to output verbose result.")
+        log_g.add_arg("test_result_path", str, "test_result", "Directory path to test result.")
+        log_g.add_arg("infer_result_path", str, "infer_result", "Directory path to infer result.")
+
+        data_g = ArgumentGroup(parser, "data", "Data paths, vocab paths and data processing options")
+        data_g.add_arg("train_data_dir", str, None, "Directory path to training data.")
+        data_g.add_arg("valid_data_dir", str, None, "Directory path to valid data.")
+        data_g.add_arg("test_data_dir", str, None, "Directory path to testing data.")
+        data_g.add_arg("infer_data_dir", str, None, "Directory path to infer data.")
+        data_g.add_arg("vocab_path", str, None, "Vocabulary path.")
+        data_g.add_arg("batch_size", int, 32, "Total examples' number in batch for training.")
+
+        run_type_g = ArgumentGroup(parser, "run_type", "running type options.")
+        run_type_g.add_arg("use_cuda", bool, False, "If set, use GPU for training.")
+        run_type_g.add_arg("task_name", str, None, "The name of task to perform sentiment classification.")
+        run_type_g.add_arg("do_train", bool, False, "Whether to perform training.")
+        run_type_g.add_arg("do_valid", bool, False, "Whether to perform dev.")
+        run_type_g.add_arg("do_test", bool, False, "Whether to perform testing.")
+        run_type_g.add_arg("do_infer", bool, False, "Whether to perform inference.")
+        run_type_g.add_arg("compute_accuracy", bool, False, "Whether to compute accuracy.")
+        run_type_g.add_arg("lamda", float, 0.91, "When task_mode is pairwise, lamda is the threshold for calculating the accuracy.")
+
+        custom_g = ArgumentGroup(parser, "customize", "customized options.")
+        self.custom_g = custom_g
+
+        parser.add_argument('--enable_ce',action='store_true',help='If set, run the task with continuous evaluation logs.')
+
+        self.parser = parser
+
+    def add_arg(self, name, dtype, default, descrip):
+        self.custom_g.add_arg(name, dtype, default, descrip)
+
+    def build_conf(self):
+        return self.parser.parse_args()
+
 
 def print_arguments(args):
     """
@@ -314,3 +362,4 @@ def init_checkpoint(exe, init_checkpoint_path, main_program):
         main_program=main_program,
         predicate=existed_persitables)
     print("Load model from {}".format(init_checkpoint_path))
+