fix double softmax ,fix test function and change default config of pairwise (#2303)

PaddleNLP/models/matching/losses/softmax_cross_entropy_loss.py

@@ -3,6 +3,7 @@ softmax loss
 """
 
 import sys
+import paddle.fluid as fluid
 
 sys.path.append("../../../")
 import models.matching.paddle_layers as layers
@@ -23,8 +24,7 @@ class SoftmaxCrossEntropyLoss(object):
         """
         compute loss
         """
-        softmax_with_cross_entropy = layers.SoftmaxWithCrossEntropyLayer()
         reduce_mean = layers.ReduceMeanLayer()
-        cost = softmax_with_cross_entropy.ops(input, label)
+        cost = fluid.layers.cross_entropy(input=input, label=label)
         avg_cost = reduce_mean.ops(cost)
         return avg_cost

PaddleNLP/models/matching/mm_dnn.py

@@ -49,10 +49,10 @@ class MMDNN(object):
             input=input,
             size=[self.vocab_size, self.emb_size],
             padding_idx=(0 if zero_pad else None),
-            param_attr=fluid.ParamAttr(name="word_embedding",
-                                       initializer=fluid.initializer.Xavier()))
+            param_attr=fluid.ParamAttr(
+                name="word_embedding", initializer=fluid.initializer.Xavier()))
         if scale:
-            emb = emb * (self.emb_size ** 0.5)
+            emb = emb * (self.emb_size**0.5)
         return emb
 
     def bi_dynamic_lstm(self, input, hidden_size):
@@ -64,7 +64,9 @@ class MMDNN(object):
                                      param_attr=fluid.ParamAttr(name="fw_fc.w"),
                                      bias_attr=False)
         forward, _ = fluid.layers.dynamic_lstm(
-            input=fw_in_proj, size=4 * hidden_size, is_reverse=False,
+            input=fw_in_proj,
+            size=4 * hidden_size,
+            is_reverse=False,
             param_attr=fluid.ParamAttr(name="forward_lstm.w"),
             bias_attr=fluid.ParamAttr(name="forward_lstm.b"))
 
@@ -73,7 +75,9 @@ class MMDNN(object):
                                      param_attr=fluid.ParamAttr(name="rv_fc.w"),
                                      bias_attr=False)
         reverse, _ = fluid.layers.dynamic_lstm(
-            input=rv_in_proj, size=4 * hidden_size, is_reverse=True,
+            input=rv_in_proj,
+            size=4 * hidden_size,
+            is_reverse=True,
             param_attr=fluid.ParamAttr(name="reverse_lstm.w"),
             bias_attr=fluid.ParamAttr(name="reverse_lstm.b"))
         return [forward, reverse]
@@ -96,7 +100,7 @@ class MMDNN(object):
 
         if mask is not None:
             cross_mask = fluid.layers.stack(x=[mask] * self.kernel_size, axis=1)
-            conv = cross_mask * conv + (1 - cross_mask) * (-2 ** 32 + 1)
+            conv = cross_mask * conv + (1 - cross_mask) * (-2**32 + 1)
         # valid padding
         pool = fluid.layers.pool2d(
             input=conv,
@@ -157,6 +161,8 @@ class MMDNN(object):
                                     act="tanh",
                                     size=self.hidden_size)
 
-        pred = fluid.layers.fc(input=relu_hid1, size=self.out_size)
+        pred = fluid.layers.fc(input=relu_hid1,
+                               size=self.out_size,
+                               act="softmax")
 
         return left_seq_encoder, pred

PaddleNLP/similarity_net/README.md

@@ -3,13 +3,13 @@
 ### 任务说明
 短文本语义匹配(SimilarityNet, SimNet)是一个计算短文本相似度的框架，可以根据用户输入的两个文本，计算出相似度得分。SimNet框架在百度各产品上广泛应用，主要包括BOW、CNN、RNN、MMDNN等核心网络结构形式，提供语义相似度计算训练和预测框架，适用于信息检索、新闻推荐、智能客服等多个应用场景，帮助企业解决语义匹配问题。可通过[AI开放平台-短文本相似度](https://ai.baidu.com/tech/nlp_basic/simnet)线上体验。
 ### 效果说明
-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0.91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0.7517。
+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|0.6766|0.7308|0.7643|1.5630|0.7517|
+|BOW_Pairwise|0.6767|0.7329|0.7650|1.5630|0.7532|
 ## 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。
@@ -46,7 +46,7 @@ tar xzf simnet_bow-pairwise-1.0.0.tar.gz -C ./model_files
 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
 sh evaluate_ecom.sh  
-sh evaluate_qqsim.sh   
+sh evaluate_qqsim.sh  
 sh evaluate_zhidao.sh  
 sh evaluate_unicom.sh  
 ```
@@ -141,7 +141,7 @@ python tokenizer.py --test_data_dir ./test.txt.utf8 --batch_size 1 > test.txt.ut
 ### 如何训练
 ```shell
 python run_classifier.py \
-   --task_name ${TASK_NAME} \ 
+   --task_name ${TASK_NAME} \
    --use_cuda false \ #是否使用GPU
    --do_train True \  #是否训练
    --do_valid True \  #是否在训练中测试开发集

PaddleNLP/similarity_net/config/bow_pointwise.json

@@ -10,8 +10,11 @@
         "class_name": "SoftmaxCrossEntropyLoss"
     },
     "optimizer": {
-        "class_name": "SGDOptimizer",
-        "learning_rate" : 0.001
+        "class_name": "AdamOptimizer",
+        "learning_rate": 0.001,
+        "beta1": 0.9,
+        "beta2": 0.999,
+        "epsilon": 1e-08
     },
     "task_mode": "pointwise",
     "model_path": "bow_pointwise"

PaddleNLP/similarity_net/config/cnn_pointwise.json

@@ -12,8 +12,11 @@
         "class_name": "SoftmaxCrossEntropyLoss"
     },
     "optimizer": {
-        "class_name": "SGDOptimizer",
-        "learning_rate" : 0.001
+        "class_name": "AdamOptimizer",
+        "learning_rate": 0.001,
+        "beta1": 0.9,
+        "beta2": 0.999,
+        "epsilon": 1e-08
     },
     "task_mode": "pointwise",
     "model_path": "cnn_pointwise"

PaddleNLP/similarity_net/config/gru_pointwise.json

@@ -11,8 +11,11 @@
         "class_name": "SoftmaxCrossEntropyLoss"
     },
     "optimizer": {
-        "class_name": "SGDOptimizer",
-        "learning_rate" : 0.001
+        "class_name": "AdamOptimizer",
+        "learning_rate" : 0.001,
+        "beta1": 0.9,
+        "beta2": 0.999,
+        "epsilon": 1e-08
     },
     "task_mode": "pointwise",
     "model_path": "gru_pointwise"

PaddleNLP/similarity_net/config/lstm_pointwise.json

@@ -11,8 +11,11 @@
         "class_name": "SoftmaxCrossEntropyLoss"
     },
     "optimizer": {
-        "class_name": "SGDOptimizer",
-        "learning_rate" : 0.001
+        "class_name": "AdamOptimizer",
+        "learning_rate": 0.001,
+        "beta1": 0.9,
+        "beta2": 0.999,
+        "epsilon": 1e-08
     },
     "task_mode": "pointwise",
     "model_path": "lstm_pointwise"

PaddleNLP/similarity_net/run.sh

@@ -38,7 +38,7 @@ train() {
 		--save_steps 1000 \
 		--validation_steps 100 \
 		--compute_accuracy False \
-		--lamda 0.91 \
+		--lamda 0.958 \
 		--task_mode ${TASK_MODE}
 }
 #run_evaluate
@@ -55,7 +55,7 @@ evaluate() {
 		--vocab_path ${VOCAB_PATH} \
 		--task_mode ${TASK_MODE} \
 		--compute_accuracy False \
-		--lamda 0.91 \
+		--lamda 0.958 \
 		--init_checkpoint ${INIT_CHECKPOINT}
 }
 # run_infer

PaddleNLP/similarity_net/run_classifier.py

@@ -26,40 +26,52 @@ 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, "examples/cnn_pointwise.json", "Init checkpoint to resume training from.")
+model_g.add_arg("config_path", str, None,
+                "Path to the json file for EmoTect model config.")
+model_g.add_arg("init_checkpoint", str, "examples/cnn_pointwise.json",
+                "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.")
+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.")
+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 = 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.")
+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("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.")
+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."
+)
 
 args = parser.parse_args()
 
@@ -75,14 +87,17 @@ def train(conf_dict, args):
     # Get data layer
     data = layers.DataLayer()
     # Load network structure dynamically
-    net = utils.import_class(
-        "../models/matching", conf_dict["net"]["module_name"], conf_dict["net"]["class_name"])(conf_dict)
+    net = utils.import_class("../models/matching",
+                             conf_dict["net"]["module_name"],
+                             conf_dict["net"]["class_name"])(conf_dict)
     # Load loss function dynamically
-    loss = utils.import_class(
-        "../models/matching/losses", conf_dict["loss"]["module_name"], conf_dict["loss"]["class_name"])(conf_dict)
+    loss = utils.import_class("../models/matching/losses",
+                              conf_dict["loss"]["module_name"],
+                              conf_dict["loss"]["class_name"])(conf_dict)
     # Load Optimization method
     optimizer = utils.import_class(
-        "../models/matching/optimizers", "paddle_optimizers", conf_dict["optimizer"]["class_name"])(conf_dict)
+        "../models/matching/optimizers", "paddle_optimizers",
+        conf_dict["optimizer"]["class_name"])(conf_dict)
     # load auc method
     metric = fluid.metrics.Auc(name="auc")
     # Get device
@@ -95,15 +110,23 @@ def train(conf_dict, args):
     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)
+        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)
         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_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_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
@@ -119,10 +142,12 @@ def train(conf_dict, args):
         left_feat, pred = net.predict(left, right)
 
         # Get Feeder and Reader
-        train_feeder = fluid.DataFeeder(place=place, feed_list=[left.name, right.name, label.name])
+        train_feeder = fluid.DataFeeder(
+            place=place, feed_list=[left.name, right.name, label.name])
         train_reader = simnet_process.get_reader("train")
         if args.do_valid:
-            valid_feeder = fluid.DataFeeder(place=place, feed_list=[left.name, right.name])
+            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)
@@ -134,8 +159,10 @@ def train(conf_dict, args):
     executor = fluid.Executor(place)
     executor.run(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())
+    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)
@@ -148,22 +175,25 @@ def train(conf_dict, args):
         batch_data = paddle.batch(reader, args.batch_size, drop_last=False)
         pred_list = []
         for data in batch_data():
-            _pred = executor.run(program=program, feed=feeder.feed(data), fetch_list=[pred.name])
+            _pred = executor.run(program=program,
+                                 feed=feeder.feed(data),
+                                 fetch_list=[pred.name])
             pred_list += list(_pred)
         pred_list = np.vstack(pred_list)
         if mode == "test":
             label_list = process.get_test_label()
         elif mode == "valid":
             label_list = process.get_valid_label()
-        if conf_dict['net']['class_name'] == 'MMDNN':
-            pred_list = utils.deal_preds_of_mmdnn(conf_dict, pred_list)
         if args.task_mode == "pairwise":
-            pred_list = np.hstack((np.ones_like(pred_list) - pred_list, pred_list))
+            pred_list = (pred_list + 1) / 2
+            pred_list = np.hstack(
+                (np.ones_like(pred_list) - pred_list, pred_list))
         metric.reset()
         metric.update(pred_list, label_list)
         auc = metric.eval()
         if args.compute_accuracy:
-            acc = utils.get_accuracy(pred_list, label_list, args.task_mode, args.lamda)
+            acc = utils.get_accuracy(pred_list, label_list, args.task_mode,
+                                     args.lamda)
             return auc, acc
         else:
             return auc
@@ -175,27 +205,41 @@ def train(conf_dict, args):
     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),
-                                        args.batch_size, drop_last=False)
+        train_batch_data = paddle.batch(
+            paddle.reader.shuffle(
+                train_reader, buf_size=10000),
+            args.batch_size,
+            drop_last=False)
         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)
+                logging.info(
+                    "the size of batch data is less than device_count(%d)" %
+                    device_count)
                 continue
             global_step += 1
-            avg_loss = parallel_executor.run([avg_cost.name], feed=train_feeder.feed(data))
+            avg_loss = parallel_executor.run([avg_cost.name],
+                                             feed=train_feeder.feed(data))
             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")
+                valid_result = valid_and_test(
+                    program=infer_program,
+                    feeder=valid_feeder,
+                    reader=valid_reader,
+                    process=simnet_process,
+                    mode="valid")
                 if args.compute_accuracy:
                     valid_auc, valid_acc = valid_result
-                    logging.info("global_steps: %d, valid_auc: %f, valid_acc: %f" % (global_step, valid_auc, valid_acc))
+                    logging.info(
+                        "global_steps: %d, valid_auc: %f, valid_acc: %f" %
+                        (global_step, valid_auc, valid_acc))
                 else:
                     valid_auc = valid_result
-                    logging.info("global_steps: %d, valid_auc: %f" % (global_step, valid_auc))
+                    logging.info("global_steps: %d, valid_auc: %f" %
+                                 (global_step, valid_auc))
             if global_step % args.save_steps == 0:
-                model_save_dir = os.path.join(args.output_dir, conf_dict["model_path"])
+                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):
@@ -204,28 +248,40 @@ def train(conf_dict, args):
                     feed_var_names = [left.name, pos_right.name]
                     target_vars = [left_feat, pos_score]
                 else:
-                    feed_var_names = [left.name, right.name, ]
+                    feed_var_names = [
+                        left.name,
+                        right.name,
+                    ]
                     target_vars = [left_feat, pred]
-                fluid.io.save_inference_model(
-                    model_path, feed_var_names, target_vars, executor, infer_program)
+                fluid.io.save_inference_model(model_path, feed_var_names,
+                                              target_vars, executor,
+                                              infer_program)
                 logging.info("saving infer model in %s" % model_path)
             losses.append(np.mean(avg_loss[0]))
         end_time = time.time()
-        logging.info("epoch: %d, loss: %f, used time: %d sec" % (epoch_id, np.mean(losses), end_time - start_time))
+        logging.info("epoch: %d, loss: %f, used time: %d sec" %
+                     (epoch_id, np.mean(losses), end_time - start_time))
     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_feeder = fluid.DataFeeder(
+                place=place, feed_list=[left.name, pos_right.name])
             test_reader = simnet_process.get_reader("test")
         else:
             # Get Feeder and Reader
-            test_feeder = fluid.DataFeeder(place=place, feed_list=[left.name, right.name])
+            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")
+        test_result = valid_and_test(
+            program=infer_program,
+            feeder=test_feeder,
+            reader=test_reader,
+            process=simnet_process,
+            mode="test")
         if args.compute_accuracy:
             test_auc, test_acc = test_result
-            logging.info("AUC of test is %f, Accuracy of test is %f" % (test_auc, test_acc))
+            logging.info("AUC of test is %f, Accuracy of test is %f" %
+                         (test_auc, test_acc))
         else:
             test_auc = test_result
             logging.info("AUC of test is %f" % test_auc)
@@ -250,46 +306,56 @@ def test(conf_dict, args):
         # Get executor
         executor = fluid.Executor(place=place)
         # Load model
-        program, feed_var_names, fetch_targets = fluid.io.load_inference_model(model_path, executor)
+        program, feed_var_names, fetch_targets = fluid.io.load_inference_model(
+            model_path, executor)
         if args.task_mode == "pairwise":
             # Get Feeder and Reader
-            feeder = fluid.DataFeeder(place=place, feed_list=feed_var_names, program=program)
+            feeder = fluid.DataFeeder(
+                place=place, feed_list=feed_var_names, program=program)
             test_reader = simnet_process.get_reader("test")
         else:
             # Get Feeder and Reader
-            feeder = fluid.DataFeeder(place=place, feed_list=feed_var_names, program=program)
+            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)
         logging.info("start test process ...")
         pred_list = []
         for iter, data in enumerate(batch_data()):
-            output = executor.run(program, feed=feeder.feed(data), fetch_list=fetch_targets)
+            output = executor.run(program,
+                                  feed=feeder.feed(data),
+                                  fetch_list=fetch_targets)
             if args.task_mode == "pairwise":
                 pred_list += list(map(lambda item: float(item[0]), output[1]))
-                predictions_file.write("\n".join(map(lambda item: str(item[0]), output[1])) + "\n")
+                predictions_file.write("\n".join(
+                    map(lambda item: str((item[0] + 1) / 2), output[1])) + "\n")
             else:
                 pred_list += map(lambda item: item, output[1])
-                predictions_file.write("\n".join(map(lambda item: str(np.argmax(item)), output[1])) + "\n")
-        if conf_dict['net']['class_name'] == 'MMDNN':
-            pred_list = utils.deal_preds_of_mmdnn(conf_dict, pred_list)
+                predictions_file.write("\n".join(
+                    map(lambda item: str(np.argmax(item)), output[1])) + "\n")
         if args.task_mode == "pairwise":
             pred_list = np.array(pred_list).reshape((-1, 1))
-            pred_list = np.hstack((np.ones_like(pred_list) - pred_list, pred_list))
+            pred_list = (pred_list + 1) / 2
+            pred_list = np.hstack(
+                (np.ones_like(pred_list) - pred_list, pred_list))
         else:
             pred_list = np.array(pred_list)
         labels = simnet_process.get_test_label()
 
         metric.update(pred_list, labels)
         if args.compute_accuracy:
-            acc = utils.get_accuracy(pred_list, labels, args.task_mode, args.lamda)
-            logging.info("AUC of test is %f, Accuracy of test is %f" % (metric.eval(), acc))
+            acc = utils.get_accuracy(pred_list, labels, args.task_mode,
+                                     args.lamda)
+            logging.info("AUC of test is %f, Accuracy of test is %f" %
+                         (metric.eval(), acc))
         else:
             logging.info("AUC of test is %f" % metric.eval())
 
     if args.verbose_result:
         utils.get_result_file(args)
-        logging.info("test result saved in %s" % os.path.join(os.getcwd(), args.test_result_path))
+        logging.info("test result saved in %s" %
+                     os.path.join(os.getcwd(), args.test_result_path))
 
 
 def infer(args):
@@ -308,29 +374,36 @@ def infer(args):
     # Get executor
     executor = fluid.Executor(place=place)
     # Load model
-    program, feed_var_names, fetch_targets = fluid.io.load_inference_model(model_path, executor)
+    program, feed_var_names, fetch_targets = fluid.io.load_inference_model(
+        model_path, executor)
     if args.task_mode == "pairwise":
         # Get Feeder and Reader
-        infer_feeder = fluid.DataFeeder(place=place, feed_list=feed_var_names, program=program)
+        infer_feeder = fluid.DataFeeder(
+            place=place, feed_list=feed_var_names, program=program)
         infer_reader = simnet_process.get_infer_reader
     else:
         # Get Feeder and Reader
-        infer_feeder = fluid.DataFeeder(place=place, feed_list=feed_var_names, program=program)
+        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)
     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)
+        output = executor.run(program,
+                              feed=infer_feeder.feed(data),
+                              fetch_list=fetch_targets)
         if args.task_mode == "pairwise":
-            preds_list += list(map(lambda item: str(item[0]), output[1]))
+            preds_list += list(
+                map(lambda item: str((item[0] + 1) / 2), output[1]))
         else:
             preds_list += map(lambda item: str(np.argmax(item)), output[1])
     with open(args.infer_result_path, "w") as infer_file:
         for _data, _pred in zip(simnet_process.get_infer_data(), preds_list):
             infer_file.write(_data + "\t" + _pred + "\n")
-    logging.info("infer result saved in %s" % os.path.join(os.getcwd(), args.infer_result_path))
+    logging.info("infer result saved in %s" %
+                 os.path.join(os.getcwd(), args.infer_result_path))
 
 
 def main(conf_dict, args):
@@ -344,7 +417,8 @@ def main(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")
+        raise ValueError(
+            "one of do_train and do_test and do_infer must be True")
 
 
 if __name__ == "__main__":

PaddleNLP/similarity_net/utils.py

@@ -11,7 +11,6 @@ import six
 import numpy as np
 import logging
 import logging.handlers
-
 """
 ******functions for file processing******
 """
@@ -165,9 +164,13 @@ def print_arguments(args):
     print('------------------------------------------------')
 
 
-def init_log(log_path, level=logging.INFO, when="D", backup=7,
-             format="%(levelname)s: %(asctime)s - %(filename)s:%(lineno)d * %(thread)d %(message)s",
-             datefmt=None):
+def init_log(
+        log_path,
+        level=logging.INFO,
+        when="D",
+        backup=7,
+        format="%(levelname)s: %(asctime)s - %(filename)s:%(lineno)d * %(thread)d %(message)s",
+        datefmt=None):
     """
     init_log - initialize log module
 
@@ -209,16 +212,14 @@ def init_log(log_path, level=logging.INFO, when="D", backup=7,
     if not os.path.isdir(dir):
         os.makedirs(dir)
 
-    handler = logging.handlers.TimedRotatingFileHandler(log_path + ".log",
-                                                        when=when,
-                                                        backupCount=backup)
+    handler = logging.handlers.TimedRotatingFileHandler(
+        log_path + ".log", when=when, backupCount=backup)
     handler.setLevel(level)
     handler.setFormatter(formatter)
     logger.addHandler(handler)
 
-    handler = logging.handlers.TimedRotatingFileHandler(log_path + ".log.wf",
-                                                        when=when,
-                                                        backupCount=backup)
+    handler = logging.handlers.TimedRotatingFileHandler(
+        log_path + ".log.wf", when=when, backupCount=backup)
     handler.setLevel(logging.WARNING)
     handler.setFormatter(formatter)
     logger.addHandler(handler)
@@ -241,7 +242,7 @@ def get_level():
     return logger.level
 
 
-def get_accuracy(preds, labels, mode, lamda=0.91):
+def get_accuracy(preds, labels, mode, lamda=0.958):
     """
     compute accuracy
     """