Add predefined networks for text classification

Add predefined networks for text classification

demo/text_classification/predict_predefine_net.py

+#coding:utf-8
+#   Copyright (c) 2019 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.
+"""Finetuning on classification task """
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import ast
+import numpy as np
+import os
+import time
+import paddle
+import paddle.fluid as fluid
+import paddlehub as hub
+
+# yapf: disable
+parser = argparse.ArgumentParser(__doc__)
+parser.add_argument("--checkpoint_dir", type=str, default=None, help="Directory to model checkpoint")
+parser.add_argument("--batch_size",     type=int,   default=1, help="Total examples' number in batch for training.")
+parser.add_argument("--max_seq_len", type=int, default=512, help="Number of words of the longest seqence.")
+parser.add_argument("--use_gpu", type=ast.literal_eval, default=False, help="Whether use GPU for finetuning, input should be True or False")
+parser.add_argument("--use_data_parallel", type=ast.literal_eval, default=False, help="Whether use data parallel.")
+parser.add_argument("--network", type=str, default='bilstm', help="Pre-defined network which was connected after Transformer model, such as ERNIE, BERT ,RoBERTa and ELECTRA.")
+args = parser.parse_args()
+# yapf: enable.
+
+if __name__ == '__main__':
+    # Load Paddlehub ERNIE Tiny pretrained model
+    module = hub.Module(name="ernie_tiny")
+    inputs, outputs, program = module.context(
+        trainable=True, max_seq_len=args.max_seq_len)
+
+    # Download dataset and use accuracy as metrics
+    # Choose dataset: GLUE/XNLI/ChinesesGLUE/NLPCC-DBQA/LCQMC
+    dataset = hub.dataset.ChnSentiCorp()
+
+    # For ernie_tiny, it use sub-word to tokenize chinese sentence
+    # If not ernie tiny, sp_model_path and word_dict_path should be set None
+    reader = hub.reader.ClassifyReader(
+        dataset=dataset,
+        vocab_path=module.get_vocab_path(),
+        max_seq_len=args.max_seq_len,
+        sp_model_path=module.get_spm_path(),
+        word_dict_path=module.get_word_dict_path())
+
+    # Construct transfer learning network
+    # Use "pooled_output" for classification tasks on an entire sentence.
+    # Use "sequence_output" for token-level output.
+    token_feature = outputs["sequence_output"]
+
+    # Setup feed list for data feeder
+    # Must feed all the tensor of module need
+    feed_list = [
+        inputs["input_ids"].name,
+        inputs["position_ids"].name,
+        inputs["segment_ids"].name,
+        inputs["input_mask"].name,
+    ]
+
+    # Setup runing config for PaddleHub Finetune API
+    config = hub.RunConfig(
+        use_data_parallel=args.use_data_parallel,
+        use_cuda=args.use_gpu,
+        batch_size=args.batch_size,
+        checkpoint_dir=args.checkpoint_dir,
+        strategy=hub.AdamWeightDecayStrategy())
+
+    # Define a classfication finetune task by PaddleHub's API
+    # network choice: bilstm, bow, cnn, dpcnn, gru, lstm (PaddleHub pre-defined network)
+    # If you wanna add network after ERNIE/BERT/RoBERTa/ELECTRA module,
+    # you must use the outputs["sequence_output"] as the token_feature of TextClassifierTask,
+    # rather than outputs["pooled_output"], and feature is None
+    cls_task = hub.TextClassifierTask(
+        data_reader=reader,
+        token_feature=token_feature,
+        feed_list=feed_list,
+        network=args.network,
+        num_classes=dataset.num_labels,
+        config=config)
+
+    # Data to be prdicted
+    data = [["这个宾馆比较陈旧了，特价的房间也很一般。总体来说一般"], ["交通方便；环境很好；服务态度很好 房间较小"],
+            ["19天硬盘就罢工了~~~算上运来的一周都没用上15天~~~可就是不能换了~~~唉~~~~你说这算什么事呀~~~"]]
+
+    print(cls_task.predict(data=data, return_result=True))

demo/text_classification/run_cls.sh

+export FLAGS_eager_delete_tensor_gb=0.0
+export CUDA_VISIBLE_DEVICES=0
+
+CKPT_DIR="./ckpt_chnsenticorp"
+
+python -u text_cls.py \
+            --batch_size=24 \
+            --use_gpu=True \
+            --checkpoint_dir=${CKPT_DIR} \
+            --learning_rate=5e-5 \
+            --weight_decay=0.01 \
+            --max_seq_len=128 \
+            --warmup_proportion=0.1 \
+            --num_epoch=3 \
+            --use_data_parallel=True
+
+# The sugguested hyper parameters for difference task
+# for ChineseGLUE:
+# TNews: batch_size=32, weight_decay=0, num_epoch=3, max_seq_len=128, lr=5e-5
+# LCQMC: batch_size=32, weight_decay=0, num_epoch=3, max_seq_len=128, lr=5e-5
+# XNLI_zh: batch_size=32, weight_decay=0, num_epoch=2, max_seq_len=128, lr=5e-5
+# INEWS: batch_size=4, weight_decay=0, num_epoch=3, max_seq_len=512, lr=5e-5
+# DRCD: see demo: reading-comprehension
+# CMRC2018: see demo: reading-comprehension
+# BQ: batch_size=32, weight_decay=0, num_epoch=2, max_seq_len=100, lr=1e-5
+# MSRANER: see demo: sequence-labeling
+# THUCNEWS: batch_size=8, weight_decay=0, num_epoch=2, max_seq_len=512, lr=5e-5
+# IFLYTEKDATA: batch_size=16, weight_decay=0, num_epoch=5, max_seq_len=256, lr=1e-5
+
+# for other tasks:
+# ChnSentiCorp: batch_size=24, weight_decay=0.01, num_epoch=3, max_seq_len=128, lr=5e-5
+# NLPCC_DBQA: batch_size=8, weight_decay=0.01, num_epoch=3, max_seq_len=512, lr=2e-5
+# LCQMC: batch_size=32, weight_decay=0, num_epoch=3, max_seq_len=128, lr=2e-5
+# QQP: batch_size=32, weight_decay=0, num_epoch=3, max_seq_len=128, lr=5e-5
+# QNLI: batch_size=32, weight_decay=0, num_epoch=3, max_seq_len=128, lr=5e-5
+# SST-2: batch_size=32, weight_decay=0, num_epoch=3, max_seq_len=128, lr=5e-5
+# CoLA: batch_size=32, weight_decay=0, num_epoch=3, max_seq_len=128, lr=5e-5
+# MRPC: batch_size=32, weight_decay=0.01, num_epoch=3, max_seq_len=128, lr=5e-5
+# RTE: batch_size=32, weight_decay=0, num_epoch=3, max_seq_len=128, lr=3e-5
+# MNLI: batch_size=32, weight_decay=0, num_epoch=3, max_seq_len=128, lr=5e-5
+#       Specify the matched/mismatched dev and test dataset  with an underscore.
+#       mnli_m or mnli: dev and test in matched dataset.
+#       mnli_mm: dev and test in mismatched dataset.
+#      The difference can be seen in https://www.nyu.edu/projects/bowman/multinli/paper.pdf.
+#       If you are not sure which one to pick, just use mnli or mnli_m.
+# XNLI: batch_size=32, weight_decay=0, num_epoch=3, max_seq_len=128, lr=5e-5
+#       Specify the language with an underscore like xnli_zh.
+#       ar- Arabic      bg- Bulgarian      de- German
+#       el- Greek       en- English        es- Spanish
+#       fr- French      hi- Hindi          ru- Russian
+#       sw- Swahili     th- Thai           tr- Turkish
+#       ur- Urdu        vi- Vietnamese     zh- Chinese (Simplified)

demo/text_classification/run_classifier.sh → demo/text_classification/run_cls_predefine_net.sh

 export FLAGS_eager_delete_tensor_gb=0.0
 export CUDA_VISIBLE_DEVICES=0
 
-CKPT_DIR="./ckpt_chnsenticorp"
+CKPT_DIR="./ckpt_chnsenticorp_predefine_net"
 
-python -u text_classifier.py \
+python -u text_cls_predefine_net.py \
                    --batch_size=24 \
                    --use_gpu=True \
                    --checkpoint_dir=${CKPT_DIR} \
@@ -12,7 +12,8 @@ python -u text_classifier.py \
                    --max_seq_len=128 \
                    --warmup_proportion=0.1 \
                    --num_epoch=3 \
-                   --use_data_parallel=True
+                   --use_data_parallel=True \
+                   --network=bilstm
 
 # The sugguested hyper parameters for difference task
 # for ChineseGLUE:

demo/text_classification/run_predict.sh

@@ -3,7 +3,8 @@ export CUDA_VISIBLE_DEVICES=0
 
 CKPT_DIR="./ckpt_chnsenticorp"
 
-python -u predict.py --checkpoint_dir=$CKPT_DIR \
-                            --max_seq_len=128 \
-                            --use_gpu=True \
-                            --batch_size=24 \
+python -u predict.py \
+            --checkpoint_dir=$CKPT_DIR \
+            --max_seq_len=128 \
+            --use_gpu=True \
+            --batch_size=24

demo/text_classification/run_predict_predefine_net.sh

+export FLAGS_eager_delete_tensor_gb=0.0
+export CUDA_VISIBLE_DEVICES=0
+
+CKPT_DIR="./ckpt_chnsenticorp_predefine_net"
+
+python -u predict_predefine_net.py \
+                    --checkpoint_dir=$CKPT_DIR \
+                    --max_seq_len=128 \
+                    --use_gpu=True \
+                    --batch_size=24 \
+                    --network=bilstm

demo/text_classification/text_cls_predefine_net.py

+#coding:utf-8
+#   Copyright (c) 2019 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.
+"""Finetuning on classification task """
+
+import argparse
+import ast
+import paddlehub as hub
+
+# yapf: disable
+parser = argparse.ArgumentParser(__doc__)
+parser.add_argument("--num_epoch", type=int, default=3, help="Number of epoches for fine-tuning.")
+parser.add_argument("--use_gpu", type=ast.literal_eval, default=True, help="Whether use GPU for finetuning, input should be True or False")
+parser.add_argument("--learning_rate", type=float, default=5e-5, help="Learning rate used to train with warmup.")
+parser.add_argument("--weight_decay", type=float, default=0.01, help="Weight decay rate for L2 regularizer.")
+parser.add_argument("--warmup_proportion", type=float, default=0.1, help="Warmup proportion params for warmup strategy")
+parser.add_argument("--checkpoint_dir", type=str, default=None, help="Directory to model checkpoint")
+parser.add_argument("--max_seq_len", type=int, default=512, help="Number of words of the longest seqence.")
+parser.add_argument("--batch_size", type=int, default=32, help="Total examples' number in batch for training.")
+parser.add_argument("--network", type=str, default='bilstm', help="Pre-defined network which was connected after Transformer model, such as ERNIE, BERT ,RoBERTa and ELECTRA.")
+parser.add_argument("--use_data_parallel", type=ast.literal_eval, default=False, help="Whether use data parallel.")
+args = parser.parse_args()
+# yapf: enable.
+
+if __name__ == '__main__':
+
+    # Load Paddlehub ERNIE Tiny pretrained model
+    module = hub.Module(name="ernie_tiny")
+    inputs, outputs, program = module.context(
+        trainable=True, max_seq_len=args.max_seq_len)
+
+    # Download dataset and use accuracy as metrics
+    # Choose dataset: GLUE/XNLI/ChinesesGLUE/NLPCC-DBQA/LCQMC
+    # metric should be acc, f1 or matthews
+    dataset = hub.dataset.ChnSentiCorp()
+    metrics_choices = ["acc"]
+
+    # For ernie_tiny, it use sub-word to tokenize chinese sentence
+    # If not ernie tiny, sp_model_path and word_dict_path should be set None
+    reader = hub.reader.ClassifyReader(
+        dataset=dataset,
+        vocab_path=module.get_vocab_path(),
+        max_seq_len=args.max_seq_len,
+        sp_model_path=module.get_spm_path(),
+        word_dict_path=module.get_word_dict_path())
+
+    # Construct transfer learning network
+    # Use "pooled_output" for classification tasks on an entire sentence.
+    # Use "sequence_output" for token-level output.
+    token_feature = outputs["sequence_output"]
+
+    # Setup feed list for data feeder
+    # Must feed all the tensor of module need
+    feed_list = [
+        inputs["input_ids"].name,
+        inputs["position_ids"].name,
+        inputs["segment_ids"].name,
+        inputs["input_mask"].name,
+    ]
+
+    # Select finetune strategy, setup config and finetune
+    strategy = hub.AdamWeightDecayStrategy(
+        warmup_proportion=args.warmup_proportion,
+        weight_decay=args.weight_decay,
+        learning_rate=args.learning_rate)
+
+    # Setup runing config for PaddleHub Finetune API
+    config = hub.RunConfig(
+        use_data_parallel=args.use_data_parallel,
+        use_cuda=args.use_gpu,
+        num_epoch=args.num_epoch,
+        batch_size=args.batch_size,
+        checkpoint_dir=args.checkpoint_dir,
+        strategy=strategy)
+
+    # Define a classfication finetune task by PaddleHub's API
+    # network choice: bilstm, bow, cnn, dpcnn, gru, lstm (PaddleHub pre-defined network)
+    # If you wanna add network after ERNIE/BERT/RoBERTa/ELECTRA module,
+    # you must use the outputs["sequence_output"] as the token_feature of TextClassifierTask,
+    # rather than outputs["pooled_output"], and feature is None
+    cls_task = hub.TextClassifierTask(
+        data_reader=reader,
+        token_feature=token_feature,
+        feed_list=feed_list,
+        network=args.network,
+        num_classes=dataset.num_labels,
+        config=config,
+        metrics_choices=metrics_choices)
+
+    # Finetune and evaluate by PaddleHub's API
+    # will finish training, evaluation, testing, save model automatically
+    cls_task.finetune_and_eval()

paddlehub/__init__.py

@@ -28,6 +28,7 @@ from . import io
 from . import dataset
 from . import finetune
 from . import reader
+from . import network
 
 from .common.dir import USER_HOME
 from .common.dir import HUB_HOME

paddlehub/finetune/__init__.py

-#coding:utf-8
-#   Copyright (c) 2019  PaddlePaddle Authors. All Rights Reserved.
+# coding:utf-8
+# Copyright (c) 2019  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.

paddlehub/finetune/task/base_task.py

@@ -998,11 +998,6 @@ class BaseTask(object):
         Returns:
             RunState: the running result of predict phase
         """
-        if not version_compare(paddle.__version__, "1.6.2") and accelerate_mode:
-            logger.warning(
-                "Fail to open predict accelerate mode as it does not support paddle < 1.6.2. Please update PaddlePaddle."
-            )
-            accelerate_mode = False
         self.accelerate_mode = accelerate_mode
 
         with self.phase_guard(phase="predict"):

paddlehub/finetune/task/classifier_task.py

@@ -17,12 +17,17 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import time
 from collections import OrderedDict
 import numpy as np
+import paddle
 import paddle.fluid as fluid
+import time
 
+from paddlehub.common.logger import logger
 from paddlehub.finetune.evaluate import calculate_f1_np, matthews_corrcoef
+from paddlehub.reader.nlp_reader import ClassifyReader
+import paddlehub.network as net
+
 from .base_task import BaseTask
 
 
@@ -104,7 +109,7 @@ class ClassifierTask(BaseTask):
             run_examples += run_state.run_examples
             run_step += run_state.run_step
             loss_sum += np.mean(
-                run_state.run_results[-1]) * run_state.run_examples
+                run_state.run_results[-2]) * run_state.run_examples
             acc_sum += np.mean(
                 run_state.run_results[2]) * run_state.run_examples
             np_labels = run_state.run_results[0]
@@ -147,7 +152,7 @@ class ClassifierTask(BaseTask):
         results = []
         for batch_state in run_states:
             batch_result = batch_state.run_results
-            batch_infer = np.argmax(batch_result, axis=2)[0]
+            batch_infer = np.argmax(batch_result[0], axis=1)
             results += [id2label[sample_infer] for sample_infer in batch_infer]
         return results
 
@@ -156,21 +161,73 @@ ImageClassifierTask = ClassifierTask
 
 
 class TextClassifierTask(ClassifierTask):
+    """
+    Create a text classification task.
+    It will use full-connect layer with softmax activation function to classify texts.
+    """
+
     def __init__(self,
-                 feature,
                  num_classes,
                  feed_list,
                  data_reader,
+                 feature=None,
+                 token_feature=None,
+                 network=None,
                  startup_program=None,
                  config=None,
                  hidden_units=None,
                  metrics_choices="default"):
+        """
+        Args:
+            num_classes: total labels of the text classification task.
+            feed_list(list): the variable name that will be feeded to the main program
+            data_reader(object): data reader for the task. It must be one of ClassifyReader and LACClassifyReader.
+            feature(Variable): the `feature` will be used to classify texts. It must be the sentence-level feature, shape as [-1, emb_size]. `Token_feature` and `feature` couldn't be setted at the same time. One of them must be setted as not None. Default None.
+            token_feature(Variable): the `feature` will be used to connect the pre-defined network. It must be the token-level feature, shape as [-1, seq_len, emb_size]. Default None.
+            network(str): the pre-defined network. Choices: 'bilstm', 'bow', 'cnn', 'dpcnn', 'gru' and 'lstm'. Default None. If network is setted, then `token_feature` must be setted and `feature` must be None.
+            main_program (object): the customized main program, default None.
+            startup_program (object): the customized startup program, default None.
+            config (RunConfig): run config for the task, such as batch_size, epoch, learning_rate setting and so on. Default None.
+            hidden_units(list): the element of `hidden_units` list is the full-connect layer size. It will add the full-connect layers to the program. Default None.
+            metrics_choices(list): metrics used to the task, default ["acc"].
+        """
+        if (not feature) and (not token_feature):
+            logger.error(
+                'Both token_feature and feature are None, one of them must be setted.'
+            )
+            exit(1)
+        elif feature and token_feature:
+            logger.error(
+                'Both token_feature and feature are setted. One should be setted, the other should be None.'
+            )
+            exit(1)
+
+        if network:
+            assert network in [
+                'bilstm', 'bow', 'cnn', 'dpcnn', 'gru', 'lstm'
+            ], 'network choice must be one of bilstm, bow, cnn, dpcnn, gru, lstm!'
+            assert token_feature and (
+                not feature
+            ), 'If you wanna use network, you must set token_feature ranther than feature for TextClassifierTask!'
+            assert len(
+                token_feature.shape
+            ) == 3, 'When you use network, the parameter token_feature must be the token-level feature, such as the sequence_output of ERNIE, BERT, RoBERTa and ELECTRA module.'
+        else:
+            assert feature and (
+                not token_feature
+            ), 'If you do not use network, you must set feature ranther than token_feature for TextClassifierTask!'
+            assert len(
+                feature.shape
+            ) == 2, 'When you do not use network, the parameter feture must be the sentence-level feature, such as the pooled_output of ERNIE, BERT, RoBERTa and ELECTRA module.'
+
+        self.network = network
 
         if metrics_choices == "default":
             metrics_choices = ["acc"]
+
         super(TextClassifierTask, self).__init__(
             data_reader=data_reader,
-            feature=feature,
+            feature=feature if feature else token_feature,
             num_classes=num_classes,
             feed_list=feed_list,
             startup_program=startup_program,
@@ -179,10 +236,33 @@ class TextClassifierTask(ClassifierTask):
             metrics_choices=metrics_choices)
 
     def _build_net(self):
-        cls_feats = fluid.layers.dropout(
-            x=self.feature,
-            dropout_prob=0.1,
-            dropout_implementation="upscale_in_train")
+        if isinstance(self._base_data_reader, ClassifyReader):
+            # ClassifyReader will return the seqence length of an input text
+            self.seq_len = fluid.layers.data(
+                name="seq_len", shape=[1], dtype='int64', lod_level=0)
+            self.seq_len_used = fluid.layers.squeeze(self.seq_len, axes=[1])
+
+            # unpad the token_feature
+            unpad_feature = fluid.layers.sequence_unpad(
+                self.feature, length=self.seq_len_used)
+
+        if self.network:
+            # add pre-defined net
+            net_func = getattr(net.classification, self.network)
+            if self.network == 'dpcnn':
+                # deepcnn network is no need to unpad
+                cls_feats = net_func(
+                    self.feature, emb_dim=self.feature.shape[-1])
+            else:
+                cls_feats = net_func(unpad_feature)
+            logger.info(
+                "%s has been added in the TextClassifierTask!" % self.network)
+        else:
+            # not use pre-defined net but to use fc net
+            cls_feats = fluid.layers.dropout(
+                x=self.feature,
+                dropout_prob=0.1,
+                dropout_implementation="upscale_in_train")
 
         if self.hidden_units is not None:
             for n_hidden in self.hidden_units:
@@ -204,6 +284,33 @@ class TextClassifierTask(ClassifierTask):
 
         return [logits]
 
+    @property
+    def feed_list(self):
+        feed_list = [varname for varname in self._base_feed_list]
+        if isinstance(self._base_data_reader, ClassifyReader):
+            # ClassifyReader will return the seqence length of an input text
+            feed_list += [self.seq_len.name]
+        if self.is_train_phase or self.is_test_phase:
+            feed_list += [self.labels[0].name]
+        return feed_list
+
+    @property
+    def fetch_list(self):
+        if self.is_train_phase or self.is_test_phase:
+            fetch_list = [
+                self.labels[0].name, self.ret_infers.name, self.metrics[0].name,
+                self.loss.name
+            ]
+        else:
+            # predict phase
+            fetch_list = [self.outputs[0].name]
+
+        if isinstance(self._base_data_reader, ClassifyReader):
+            # to avoid save_inference_model to prune seq_len variable
+            fetch_list += [self.seq_len.name]
+
+        return fetch_list
+
 
 class MultiLabelClassifierTask(ClassifierTask):
     def __init__(self,

paddlehub/finetune/task/sequence_task.py

@@ -66,11 +66,7 @@ class SequenceLabelTask(BaseTask):
     def _build_net(self):
         self.seq_len = fluid.layers.data(
             name="seq_len", shape=[1], dtype='int64', lod_level=0)
-
-        if version_compare(paddle.__version__, "1.6"):
-            self.seq_len_used = fluid.layers.squeeze(self.seq_len, axes=[1])
-        else:
-            self.seq_len_used = self.seq_len
+        self.seq_len_used = fluid.layers.squeeze(self.seq_len, axes=[1])
 
         if self.add_crf:
             unpad_feature = fluid.layers.sequence_unpad(

paddlehub/network/__init__.py

+# coding:utf-8
+# Copyright (c) 2019  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 . import classification

paddlehub/network/classification.py

+# coding:utf-8
+# 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.
+"""
+This module provide nets for text classification
+"""
+
+import paddle
+import paddle.fluid as fluid
+
+
+def bilstm(token_embeddings, hid_dim=128, hid_dim2=96):
+    """
+    bilstm net
+    """
+    fc0 = fluid.layers.fc(input=token_embeddings, size=hid_dim * 4)
+    rfc0 = fluid.layers.fc(input=token_embeddings, size=hid_dim * 4)
+    lstm_h, c = fluid.layers.dynamic_lstm(
+        input=fc0, size=hid_dim * 4, is_reverse=False)
+    rlstm_h, c = fluid.layers.dynamic_lstm(
+        input=rfc0, size=hid_dim * 4, is_reverse=True)
+    lstm_last = fluid.layers.sequence_last_step(input=lstm_h)
+    rlstm_last = fluid.layers.sequence_last_step(input=rlstm_h)
+    lstm_last_tanh = fluid.layers.tanh(lstm_last)
+    rlstm_last_tanh = fluid.layers.tanh(rlstm_last)
+
+    # concat layer
+    lstm_concat = fluid.layers.concat(input=[lstm_last, rlstm_last], axis=1)
+    # full connect layer
+    fc = fluid.layers.fc(input=lstm_concat, size=hid_dim2, act='tanh')
+    return fc
+
+
+def bow(token_embeddings, hid_dim=128, hid_dim2=96):
+    """
+    bow net
+    """
+    # bow layer
+    bow = fluid.layers.sequence_pool(input=token_embeddings, pool_type='sum')
+    bow_tanh = fluid.layers.tanh(bow)
+    # full connect layer
+    fc_1 = fluid.layers.fc(input=bow_tanh, size=hid_dim, act="tanh")
+    fc_2 = fluid.layers.fc(input=fc_1, size=hid_dim2, act="tanh")
+    return fc_2
+
+
+def cnn(token_embeddings, hid_dim=128, win_size=3):
+    """
+    cnn net
+    """
+    # cnn layer
+    conv = fluid.nets.sequence_conv_pool(
+        input=token_embeddings,
+        num_filters=hid_dim,
+        filter_size=win_size,
+        act="tanh",
+        pool_type="max")
+    # full connect layer
+    fc_1 = fluid.layers.fc(input=conv, size=hid_dim)
+    return fc_1
+
+
+def dpcnn(token_embeddings,
+          hid_dim=128,
+          channel_size=250,
+          emb_dim=1024,
+          blocks=6):
+    """
+    deepcnn net
+    """
+
+    def _block(x):
+        x = fluid.layers.relu(x)
+        x = fluid.layers.conv2d(x, channel_size, (3, 1), padding=(1, 0))
+        x = fluid.layers.relu(x)
+        x = fluid.layers.conv2d(x, channel_size, (3, 1), padding=(1, 0))
+        return x
+
+    emb = fluid.layers.unsqueeze(token_embeddings, axes=[1])
+    region_embedding = fluid.layers.conv2d(
+        emb, channel_size, (3, emb_dim), padding=(1, 0))
+    conv_features = _block(region_embedding)
+    conv_features = conv_features + region_embedding
+    # multi-cnn layer
+    for i in range(blocks):
+        block_features = fluid.layers.pool2d(
+            conv_features,
+            pool_size=(3, 1),
+            pool_stride=(2, 1),
+            pool_padding=(1, 0))
+        conv_features = _block(block_features)
+        conv_features = block_features + conv_features
+    features = fluid.layers.pool2d(conv_features, global_pooling=True)
+    features = fluid.layers.squeeze(features, axes=[2, 3])
+    # full connect layer
+    fc_1 = fluid.layers.fc(input=features, size=hid_dim, act="tanh")
+    return fc_1
+
+
+def gru(token_embeddings, hid_dim=128, hid_dim2=96):
+    """
+    gru net
+    """
+    fc0 = fluid.layers.fc(input=token_embeddings, size=hid_dim * 3)
+    gru_h = fluid.layers.dynamic_gru(input=fc0, size=hid_dim, is_reverse=False)
+    gru_max = fluid.layers.sequence_pool(input=gru_h, pool_type='max')
+    gru_max_tanh = fluid.layers.tanh(gru_max)
+    fc1 = fluid.layers.fc(input=gru_max_tanh, size=hid_dim2, act='tanh')
+    return fc1
+
+
+def lstm(token_embeddings, hid_dim=128, hid_dim2=96):
+    """
+    lstm net
+    """
+    # lstm layer
+    fc0 = fluid.layers.fc(input=token_embeddings, size=hid_dim * 4)
+    lstm_h, c = fluid.layers.dynamic_lstm(
+        input=fc0, size=hid_dim * 4, is_reverse=False)
+
+    # max pooling layer
+    lstm_max = fluid.layers.sequence_pool(input=lstm_h, pool_type='max')
+    lstm_max_tanh = fluid.layers.tanh(lstm_max)
+
+    # full connect layer
+    fc1 = fluid.layers.fc(input=lstm_max_tanh, size=hid_dim2, act='tanh')
+    return fc1

paddlehub/reader/nlp_reader.py

@@ -65,7 +65,6 @@ class BaseNLPReader(BaseReader):
             logger.warning(
                 "use_task_id has been de discarded since PaddleHub v1.4.0, it's no necessary to feed task_ids now."
             )
-            self.task_id = 0
 
         self.Record_With_Label_Id = namedtuple(
             'Record',
@@ -272,11 +271,12 @@ class ClassifyReader(BaseNLPReader):
         batch_text_type_ids = [record.text_type_ids for record in batch_records]
         batch_position_ids = [record.position_ids for record in batch_records]
 
-        padded_token_ids, input_mask = pad_batch_data(
+        padded_token_ids, input_mask, batch_seq_lens = pad_batch_data(
             batch_token_ids,
             max_seq_len=self.max_seq_len,
             pad_idx=self.pad_id,
-            return_input_mask=True)
+            return_input_mask=True,
+            return_seq_lens=True)
         padded_text_type_ids = pad_batch_data(
             batch_text_type_ids,
             max_seq_len=self.max_seq_len,
@@ -286,36 +286,16 @@ class ClassifyReader(BaseNLPReader):
             max_seq_len=self.max_seq_len,
             pad_idx=self.pad_id)
 
+        return_list = [
+            padded_token_ids, padded_position_ids, padded_text_type_ids,
+            input_mask, batch_seq_lens
+        ]
         if phase != "predict":
             batch_labels = [record.label_id for record in batch_records]
             batch_labels = np.array(batch_labels).astype("int64").reshape(
                 [-1, 1])
+            return_list += [batch_labels]
 
-            return_list = [
-                padded_token_ids, padded_position_ids, padded_text_type_ids,
-                input_mask, batch_labels
-            ]
-
-            if self.use_task_id:
-                padded_task_ids = np.ones_like(
-                    padded_token_ids, dtype="int64") * self.task_id
-                return_list = [
-                    padded_token_ids, padded_position_ids, padded_text_type_ids,
-                    input_mask, padded_task_ids, batch_labels
-                ]
-        else:
-            return_list = [
-                padded_token_ids, padded_position_ids, padded_text_type_ids,
-                input_mask
-            ]
-
-            if self.use_task_id:
-                padded_task_ids = np.ones_like(
-                    padded_token_ids, dtype="int64") * self.task_id
-                return_list = [
-                    padded_token_ids, padded_position_ids, padded_text_type_ids,
-                    input_mask, padded_task_ids
-                ]
         return return_list
 
 
@@ -369,40 +349,20 @@ class SequenceLabelReader(BaseNLPReader):
             max_seq_len=self.max_seq_len,
             pad_idx=self.pad_id)
 
+        return_list = [
+            padded_token_ids, padded_position_ids, padded_text_type_ids,
+            input_mask
+        ]
         if phase != "predict":
             batch_label_ids = [record.label_id for record in batch_records]
             padded_label_ids = pad_batch_data(
                 batch_label_ids,
                 max_seq_len=self.max_seq_len,
                 pad_idx=len(self.label_map) - 1)
-
-            return_list = [
-                padded_token_ids, padded_position_ids, padded_text_type_ids,
-                input_mask, padded_label_ids, batch_seq_lens
-            ]
-
-            if self.use_task_id:
-                padded_task_ids = np.ones_like(
-                    padded_token_ids, dtype="int64") * self.task_id
-                return_list = [
-                    padded_token_ids, padded_position_ids, padded_text_type_ids,
-                    input_mask, padded_task_ids, padded_label_ids,
-                    batch_seq_lens
-                ]
+            return_list += [padded_label_ids, batch_seq_lens]
 
         else:
-            return_list = [
-                padded_token_ids, padded_position_ids, padded_text_type_ids,
-                input_mask, batch_seq_lens
-            ]
-
-            if self.use_task_id:
-                padded_task_ids = np.ones_like(
-                    padded_token_ids, dtype="int64") * self.task_id
-                return_list = [
-                    padded_token_ids, padded_position_ids, padded_text_type_ids,
-                    input_mask, padded_task_ids, batch_seq_lens
-                ]
+            return_list += [batch_seq_lens]
 
         return return_list
 
@@ -514,37 +474,18 @@ class MultiLabelClassifyReader(BaseNLPReader):
             max_seq_len=self.max_seq_len,
             pad_idx=self.pad_id)
 
+        return_list = [
+            padded_token_ids, padded_position_ids, padded_text_type_ids,
+            input_mask
+        ]
         if phase != "predict":
             batch_labels_ids = [record.label_id for record in batch_records]
             num_label = len(self.dataset.get_labels())
             batch_labels = np.array(batch_labels_ids).astype("int64").reshape(
                 [-1, num_label])
 
-            return_list = [
-                padded_token_ids, padded_position_ids, padded_text_type_ids,
-                input_mask, batch_labels
-            ]
+            return_list += [batch_labels]
 
-            if self.use_task_id:
-                padded_task_ids = np.ones_like(
-                    padded_token_ids, dtype="int64") * self.task_id
-                return_list = [
-                    padded_token_ids, padded_position_ids, padded_text_type_ids,
-                    input_mask, padded_task_ids, batch_labels
-                ]
-        else:
-            return_list = [
-                padded_token_ids, padded_position_ids, padded_text_type_ids,
-                input_mask
-            ]
-
-            if self.use_task_id:
-                padded_task_ids = np.ones_like(
-                    padded_token_ids, dtype="int64") * self.task_id
-                return_list = [
-                    padded_token_ids, padded_position_ids, padded_text_type_ids,
-                    input_mask, padded_task_ids
-                ]
         return return_list
 
     def _convert_example_to_record(self,
@@ -634,37 +575,17 @@ class RegressionReader(BaseNLPReader):
             max_seq_len=self.max_seq_len,
             pad_idx=self.pad_id)
 
+        return_list = [
+            padded_token_ids, padded_position_ids, padded_text_type_ids,
+            input_mask
+        ]
         if phase != "predict":
             batch_labels = [record.label_id for record in batch_records]
             # the only diff with ClassifyReader: astype("float32")
             batch_labels = np.array(batch_labels).astype("float32").reshape(
                 [-1, 1])
 
-            return_list = [
-                padded_token_ids, padded_position_ids, padded_text_type_ids,
-                input_mask, batch_labels
-            ]
-
-            if self.use_task_id:
-                padded_task_ids = np.ones_like(
-                    padded_token_ids, dtype="int64") * self.task_id
-                return_list = [
-                    padded_token_ids, padded_position_ids, padded_text_type_ids,
-                    input_mask, padded_task_ids, batch_labels
-                ]
-        else:
-            return_list = [
-                padded_token_ids, padded_position_ids, padded_text_type_ids,
-                input_mask
-            ]
-
-            if self.use_task_id:
-                padded_task_ids = np.ones_like(
-                    padded_token_ids, dtype="int64") * self.task_id
-                return_list = [
-                    padded_token_ids, padded_position_ids, padded_text_type_ids,
-                    input_mask, padded_task_ids
-                ]
+            return_list += [batch_labels]
 
         return return_list
 
@@ -831,6 +752,10 @@ class ReadingComprehensionReader(BaseNLPReader):
             pad_idx=self.pad_id,
             max_seq_len=self.max_seq_len)
 
+        return_list = [
+            padded_token_ids, padded_position_ids, padded_text_type_ids,
+            input_mask, batch_unique_ids
+        ]
         if phase != "predict":
             batch_start_position = [
                 record.start_position for record in batch_records
@@ -843,33 +768,8 @@ class ReadingComprehensionReader(BaseNLPReader):
             batch_end_position = np.array(batch_end_position).astype(
                 "int64").reshape([-1, 1])
 
-            return_list = [
-                padded_token_ids, padded_position_ids, padded_text_type_ids,
-                input_mask, batch_unique_ids, batch_start_position,
-                batch_end_position
-            ]
+            return_list += [batch_start_position, batch_end_position]
 
-            if self.use_task_id:
-                padded_task_ids = np.ones_like(
-                    padded_token_ids, dtype="int64") * self.task_id
-                return_list = [
-                    padded_token_ids, padded_position_ids, padded_text_type_ids,
-                    input_mask, padded_task_ids, batch_unique_ids,
-                    batch_start_position, batch_end_position
-                ]
-
-        else:
-            return_list = [
-                padded_token_ids, padded_position_ids, padded_text_type_ids,
-                input_mask, batch_unique_ids
-            ]
-            if self.use_task_id:
-                padded_task_ids = np.ones_like(
-                    padded_token_ids, dtype="int64") * self.task_id
-                return_list = [
-                    padded_token_ids, padded_position_ids, padded_text_type_ids,
-                    input_mask, padded_task_ids, batch_unique_ids
-                ]
         return return_list
 
     def _prepare_batch_data(self, records, batch_size, phase=None):