Merge branch 'master' of https://github.com/PaddlePaddle/hapi into add-hapi-seq2seq

.gitignore

@@ -2,3 +2,6 @@
 *.json
 output*
 *checkpoint*
+build
+dist
+hapi.egg-info

examples/bert/bert.yaml

+bert_config_path: "./config/bert_config.json"
+init_checkpoint: None
+init_pretraining_params: None
+checkpoints: "./saved_model"
+epoch: 3
+learning_rate: 0.0001
+lr_scheduler: "linear_warmup_decay"
+weight_decay: 0.01
+warmup_proportion: 0.1
+save_steps: 100000
+validation_steps: 100000
+loss_scaling: 1.0
+skip_steps: 100
+data_dir: None
+vocab_path: None
+max_seq_len: 512
+batch_size: 32
+in_tokens: False
+do_lower_case: True
+random_seed: 5512
+use_cuda: True
+shuffle: True
+do_train: True
+do_test: True
+use_data_parallel: False
+verbose: False
+

examples/bert/bert_classifier.py

+#   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.
+"""BERT fine-tuning in Paddle Dygraph Mode."""
+
+import paddle.fluid as fluid
+from hapi.metrics import Accuracy
+from hapi.configure import Config
+from hapi.text.bert import BertEncoder
+from paddle.fluid.dygraph import Linear, Layer
+from hapi.model import set_device, Model, SoftmaxWithCrossEntropy, Input
+import hapi.text.tokenizer.tokenization as tokenization
+from hapi.text.bert import Optimizer, BertConfig, BertDataLoader, BertInputExample
+
+
+class ClsModelLayer(Model):
+    """
+    classify model
+    """
+
+    def __init__(self,
+                 args,
+                 config,
+                 num_labels,
+                 return_pooled_out=True,
+                 use_fp16=False):
+        super(ClsModelLayer, self).__init__()
+        self.config = config
+        self.use_fp16 = use_fp16
+        self.loss_scaling = args.loss_scaling
+
+        self.bert_layer = BertEncoder(
+            config=self.config, return_pooled_out=True, use_fp16=self.use_fp16)
+
+        self.cls_fc = Linear(
+            input_dim=self.config["hidden_size"],
+            output_dim=num_labels,
+            param_attr=fluid.ParamAttr(
+                name="cls_out_w",
+                initializer=fluid.initializer.TruncatedNormal(scale=0.02)),
+            bias_attr=fluid.ParamAttr(
+                name="cls_out_b", initializer=fluid.initializer.Constant(0.)))
+
+    def forward(self, src_ids, position_ids, sentence_ids, input_mask):
+        """
+        forward
+        """
+
+        enc_output, next_sent_feat = self.bert_layer(src_ids, position_ids,
+                                                     sentence_ids, input_mask)
+
+        cls_feats = fluid.layers.dropout(
+            x=next_sent_feat,
+            dropout_prob=0.1,
+            dropout_implementation="upscale_in_train")
+
+        pred = self.cls_fc(cls_feats)
+
+        return pred
+
+
+def main():
+
+    config = Config(yaml_file="./bert.yaml")
+    config.build()
+    config.Print()
+
+    device = set_device("gpu" if config.use_cuda else "cpu")
+    fluid.enable_dygraph(device)
+
+    bert_config = BertConfig(config.bert_config_path)
+    bert_config.print_config()
+
+    tokenizer = tokenization.FullTokenizer(
+        vocab_file=config.vocab_path, do_lower_case=config.do_lower_case)
+
+    def mnli_line_processor(line_id, line):
+        if line_id == "0":
+            return None
+        uid = tokenization.convert_to_unicode(line[0])
+        text_a = tokenization.convert_to_unicode(line[8])
+        text_b = tokenization.convert_to_unicode(line[9])
+        label = tokenization.convert_to_unicode(line[-1])
+        if label not in ["contradiction", "entailment", "neutral"]:
+            label = "contradiction"
+        return BertInputExample(
+            uid=uid, text_a=text_a, text_b=text_b, label=label)
+
+    train_dataloader = BertDataLoader(
+        "./data/glue_data/MNLI/train.tsv",
+        tokenizer, ["contradiction", "entailment", "neutral"],
+        max_seq_length=config.max_seq_len,
+        batch_size=config.batch_size,
+        line_processor=mnli_line_processor)
+
+    test_dataloader = BertDataLoader(
+        "./data/glue_data/MNLI/dev_matched.tsv",
+        tokenizer, ["contradiction", "entailment", "neutral"],
+        max_seq_length=config.max_seq_len,
+        batch_size=config.batch_size,
+        line_processor=mnli_line_processor,
+        shuffle=False,
+        phase="predict")
+
+    trainer_count = fluid.dygraph.parallel.Env().nranks
+    num_train_examples = len(train_dataloader.dataset)
+    max_train_steps = config.epoch * num_train_examples // config.batch_size // trainer_count
+    warmup_steps = int(max_train_steps * config.warmup_proportion)
+
+    print("Trainer count: %d" % trainer_count)
+    print("Num train examples: %d" % num_train_examples)
+    print("Max train steps: %d" % max_train_steps)
+    print("Num warmup steps: %d" % warmup_steps)
+
+    inputs = [
+        Input(
+            [None, None], 'int64', name='src_ids'), Input(
+                [None, None], 'int64', name='pos_ids'), Input(
+                    [None, None], 'int64', name='sent_ids'), Input(
+                        [None, None], 'float32', name='input_mask')
+    ]
+
+    labels = [Input([None, 1], 'int64', name='label')]
+
+    cls_model = ClsModelLayer(
+        config,
+        bert_config,
+        len(["contradiction", "entailment", "neutral"]),
+        return_pooled_out=True)
+
+    optimizer = Optimizer(
+        warmup_steps=warmup_steps,
+        num_train_steps=max_train_steps,
+        learning_rate=config.learning_rate,
+        model_cls=cls_model,
+        weight_decay=config.weight_decay,
+        scheduler=config.lr_scheduler,
+        loss_scaling=config.loss_scaling,
+        parameter_list=cls_model.parameters())
+
+    cls_model.prepare(
+        optimizer,
+        SoftmaxWithCrossEntropy(),
+        Accuracy(topk=(1, 2)),
+        inputs,
+        labels,
+        device=device)
+
+    cls_model.bert_layer.init_parameters(
+        config.init_pretraining_params, verbose=config.verbose)
+
+    # do train
+    cls_model.fit(train_data=train_dataloader.dataloader,
+                  epochs=config.epoch,
+                  save_dir=config.checkpoints)
+
+    # do eval
+    cls_model.evaluate(
+        eval_data=test_dataloader.dataloader, batch_size=config.batch_size)
+
+
+if __name__ == '__main__':
+    main()

examples/bert/run_classifier_single_gpu.sh

+#!/bin/bash
+BERT_BASE_PATH="./data/pretrained_models/uncased_L-12_H-768_A-12/"
+TASK_NAME='MNLI'
+DATA_PATH="./data/glue_data/MNLI/"
+CKPT_PATH="./data/saved_model/mnli_models"
+
+export CUDA_VISIBLE_DEVICES=0
+
+# start fine-tuning
+python3.7 bert_classifier.py\
+    --use_cuda true \
+    --do_train true \
+    --do_test true \
+    --batch_size 64 \
+    --init_pretraining_params ${BERT_BASE_PATH}/dygraph_params/ \
+    --data_dir ${DATA_PATH} \
+    --vocab_path ${BERT_BASE_PATH}/vocab.txt \
+    --checkpoints ${CKPT_PATH} \
+    --save_steps 1000 \
+    --weight_decay  0.01 \
+    --warmup_proportion 0.1 \
+    --validation_steps 100 \
+    --epoch 3 \
+    --max_seq_len 128 \
+    --bert_config_path ${BERT_BASE_PATH}/bert_config.json \
+    --learning_rate 5e-5 \
+    --skip_steps 10 \
+    --shuffle true
+

examples/bert_leveldb/bert.yaml

+bert_config_path: "./config/bert_config.json"
+init_checkpoint: None
+init_pretraining_params: None
+checkpoints: "./saved_model"
+epoch: 3
+learning_rate: 0.0001
+lr_scheduler: "linear_warmup_decay"
+weight_decay: 0.01
+warmup_proportion: 0.1
+save_steps: 100000
+validation_steps: 100000
+loss_scaling: 1.0
+skip_steps: 100
+data_dir: None
+vocab_path: None
+max_seq_len: 512
+batch_size: 32
+in_tokens: False
+do_lower_case: True
+random_seed: 5512
+use_cuda: True
+shuffle: True
+do_train: True
+do_test: True
+use_data_parallel: False
+verbose: False
+

examples/bert_leveldb/bert_classifier.py

+#   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.
+"""BERT fine-tuning in Paddle Dygraph Mode."""
+
+import paddle.fluid as fluid
+from hapi.metrics import Accuracy
+from hapi.configure import Config
+from hapi.text.bert import BertEncoder
+from paddle.fluid.dygraph import Linear, Layer
+from hapi.model import set_device, Model, SoftmaxWithCrossEntropy, Input
+import hapi.text.tokenizer.tokenization as tokenization
+from hapi.text.bert import Optimizer, BertConfig, BertDataLoader, BertInputExample
+
+
+class ClsModelLayer(Model):
+    """
+    classify model
+    """
+
+    def __init__(self,
+                 args,
+                 config,
+                 num_labels,
+                 return_pooled_out=True,
+                 use_fp16=False):
+        super(ClsModelLayer, self).__init__()
+        self.config = config
+        self.use_fp16 = use_fp16
+        self.loss_scaling = args.loss_scaling
+
+        self.bert_layer = BertEncoder(
+            config=self.config, return_pooled_out=True, use_fp16=self.use_fp16)
+
+        self.cls_fc = Linear(
+            input_dim=self.config["hidden_size"],
+            output_dim=num_labels,
+            param_attr=fluid.ParamAttr(
+                name="cls_out_w",
+                initializer=fluid.initializer.TruncatedNormal(scale=0.02)),
+            bias_attr=fluid.ParamAttr(
+                name="cls_out_b", initializer=fluid.initializer.Constant(0.)))
+
+    def forward(self, src_ids, position_ids, sentence_ids, input_mask):
+        """
+        forward
+        """
+
+        enc_output, next_sent_feat = self.bert_layer(src_ids, position_ids,
+                                                     sentence_ids, input_mask)
+
+        cls_feats = fluid.layers.dropout(
+            x=next_sent_feat,
+            dropout_prob=0.1,
+            dropout_implementation="upscale_in_train")
+
+        pred = self.cls_fc(cls_feats)
+
+        return pred
+
+
+def main():
+
+    config = Config(yaml_file="./bert.yaml")
+    config.build()
+    config.Print()
+
+    device = set_device("gpu" if config.use_cuda else "cpu")
+    fluid.enable_dygraph(device)
+
+    bert_config = BertConfig(config.bert_config_path)
+    bert_config.print_config()
+
+    tokenizer = tokenization.FullTokenizer(
+        vocab_file=config.vocab_path, do_lower_case=config.do_lower_case)
+
+    def mnli_line_processor(line_id, line):
+        if line_id == "0":
+            return None
+        uid = tokenization.convert_to_unicode(line[0])
+        text_a = tokenization.convert_to_unicode(line[8])
+        text_b = tokenization.convert_to_unicode(line[9])
+        label = tokenization.convert_to_unicode(line[-1])
+        if label not in ["contradiction", "entailment", "neutral"]:
+            label = "contradiction"
+        return BertInputExample(
+            uid=uid, text_a=text_a, text_b=text_b, label=label)
+
+    train_dataloader = BertDataLoader(
+        "./data/glue_data/MNLI/train.tsv",
+        tokenizer, ["contradiction", "entailment", "neutral"],
+        max_seq_length=config.max_seq_len,
+        batch_size=config.batch_size,
+        line_processor=mnli_line_processor,
+        mode="leveldb",
+        phase="train")
+
+    test_dataloader = BertDataLoader(
+        "./data/glue_data/MNLI/dev_matched.tsv",
+        tokenizer, ["contradiction", "entailment", "neutral"],
+        max_seq_length=config.max_seq_len,
+        batch_size=config.batch_size,
+        line_processor=mnli_line_processor,
+        shuffle=False,
+        phase="predict")
+
+    trainer_count = fluid.dygraph.parallel.Env().nranks
+    num_train_examples = len(train_dataloader.dataset)
+    max_train_steps = config.epoch * num_train_examples // config.batch_size // trainer_count
+    warmup_steps = int(max_train_steps * config.warmup_proportion)
+
+    print("Trainer count: %d" % trainer_count)
+    print("Num train examples: %d" % num_train_examples)
+    print("Max train steps: %d" % max_train_steps)
+    print("Num warmup steps: %d" % warmup_steps)
+
+    inputs = [
+        Input(
+            [None, None], 'int64', name='src_ids'), Input(
+                [None, None], 'int64', name='pos_ids'), Input(
+                    [None, None], 'int64', name='sent_ids'), Input(
+                        [None, None], 'float32', name='input_mask')
+    ]
+
+    labels = [Input([None, 1], 'int64', name='label')]
+
+    cls_model = ClsModelLayer(
+        config,
+        bert_config,
+        len(["contradiction", "entailment", "neutral"]),
+        return_pooled_out=True)
+
+    optimizer = Optimizer(
+        warmup_steps=warmup_steps,
+        num_train_steps=max_train_steps,
+        learning_rate=config.learning_rate,
+        model_cls=cls_model,
+        weight_decay=config.weight_decay,
+        scheduler=config.lr_scheduler,
+        loss_scaling=config.loss_scaling,
+        parameter_list=cls_model.parameters())
+
+    cls_model.prepare(
+        optimizer,
+        SoftmaxWithCrossEntropy(),
+        Accuracy(topk=(1, 2)),
+        inputs,
+        labels,
+        device=device)
+
+    cls_model.bert_layer.init_parameters(
+        config.init_pretraining_params, verbose=config.verbose)
+
+    # do train
+    cls_model.fit(train_data=train_dataloader.dataloader,
+                  epochs=config.epoch,
+                  save_dir=config.checkpoints)
+
+    # do eval
+    cls_model.evaluate(
+        eval_data=test_dataloader.dataloader, batch_size=config.batch_size)
+
+
+if __name__ == '__main__':
+    main()

examples/bert_leveldb/run_classifier_multi_gpu.sh

+#!/bin/bash
+BERT_BASE_PATH="./data/pretrained_models/uncased_L-12_H-768_A-12/"
+TASK_NAME='MNLI'
+DATA_PATH="./data/glue_data/MNLI/"
+CKPT_PATH="./data/saved_model/mnli_models"
+
+# start fine-tuning
+python3.7 -m paddle.distributed.launch --started_port 8899 --selected_gpus=0,1,2,3 bert_classifier.py\
+    --use_cuda true \
+    --do_train true \
+    --do_test true \
+    --batch_size 64 \
+    --init_pretraining_params ${BERT_BASE_PATH}/dygraph_params/ \
+    --data_dir ${DATA_PATH} \
+    --vocab_path ${BERT_BASE_PATH}/vocab.txt \
+    --checkpoints ${CKPT_PATH} \
+    --save_steps 1000 \
+    --weight_decay  0.01 \
+    --warmup_proportion 0.1 \
+    --validation_steps 100 \
+    --epoch 3 \
+    --max_seq_len 128 \
+    --bert_config_path ${BERT_BASE_PATH}/bert_config.json \
+    --learning_rate 5e-5 \
+    --skip_steps 10 \
+    --shuffle true
+

examples/bert_leveldb/run_classifier_single_gpu.sh

+#!/bin/bash
+BERT_BASE_PATH="./data/pretrained_models/uncased_L-12_H-768_A-12/"
+TASK_NAME='MNLI'
+DATA_PATH="./data/glue_data/MNLI/"
+CKPT_PATH="./data/saved_model/mnli_models"
+
+export CUDA_VISIBLE_DEVICES=0
+
+# start fine-tuning
+python3.7 bert_classifier.py\
+    --use_cuda true \
+    --do_train true \
+    --do_test true \
+    --batch_size 64 \
+    --init_pretraining_params ${BERT_BASE_PATH}/dygraph_params/ \
+    --data_dir ${DATA_PATH} \
+    --vocab_path ${BERT_BASE_PATH}/vocab.txt \
+    --checkpoints ${CKPT_PATH} \
+    --save_steps 1000 \
+    --weight_decay  0.01 \
+    --warmup_proportion 0.1 \
+    --validation_steps 100 \
+    --epoch 3 \
+    --max_seq_len 128 \
+    --bert_config_path ${BERT_BASE_PATH}/bert_config.json \
+    --learning_rate 5e-5 \
+    --skip_steps 10 \
+    --shuffle true
+

bmn/README.md → examples/bmn/README.md

@@ -29,7 +29,6 @@ BMN Overview
 ├── train.py           # 训练代码，训练网络
 ├── eval.py            # 评估代码，评估网络性能
 ├── predict.py         # 预测代码，针对任意输入预测结果
-├── bmn_model.py       # 网络结构与损失函数定义
 ├── bmn_metric.py      # 精度评估方法定义
 ├── reader.py          # 数据reader，构造Dataset和Dataloader
 ├── bmn_utils.py       # 模型细节相关代码
@@ -41,7 +40,7 @@ BMN Overview
 
 ## 数据准备
 
-BMN的训练数据采用ActivityNet1.3提供的数据集，我们提供了处理好的视频特征，请下载[bmn\_feat](https://paddlemodels.bj.bcebos.com/video_detection/bmn_feat.tar.gz)数据后解压，同时相应的修改bmn.yaml中的特征路径feat\_path。对应的标签文件请下载[label](https://paddlemodels.bj.bcebos.com/video_detection/activitynet_1.3_annotations.json)并修改bmn.yaml中的标签文件路径anno\_file。
+BMN的训练数据采用ActivityNet1.3提供的数据集，我们提供了处理好的视频特征和对应的标签文件，请下载特征数据[bmn\_feat](https://paddlemodels.bj.bcebos.com/video_detection/bmn_feat.tar.gz)和标签数据[label](https://paddlemodels.bj.bcebos.com/video_detection/activitynet_1.3_annotations.json)，并相应地修改配置文件bmn.yaml中的特征文件路径feat\_path和标签文件路径anno\_file。
 
 
 ## 模型训练
@@ -52,22 +51,17 @@ BMN的训练数据采用ActivityNet1.3提供的数据集，我们提供了处理
 
     bash run.sh
 
-若使用单卡训练，启动方式如下:
+若使用单卡训练，请将配置文件bmn.yaml中的batch\_size调整为16，启动方式如下:
 
-    export CUDA_VISIBLE_DEVICES=0
     python train.py
 
-- 代码运行需要先安装pandas
-
-- 从头开始训练，使用上述启动命令行或者脚本程序即可启动训练，不需要用到预训练模型
+默认使用静态图训练，若使用动态图训练只需要在运行脚本添加`-d`参数即可，如：
 
-- 单卡训练时，请将配置文件中的batch_size调整为16
+    python train.py -d
 
-**训练策略：**
+- 代码运行需要先安装pandas
 
-*  采用Adam优化器，初始learning\_rate=0.001
-*  权重衰减系数为1e-4
-*  学习率在迭代次数达到4200的时候做一次衰减，衰减系数为0.1
+- 从头开始训练，使用上述启动命令行或者脚本程序即可启动训练，不需要用到预训练模型
 
 
 ## 模型评估
@@ -76,9 +70,9 @@ BMN的训练数据采用ActivityNet1.3提供的数据集，我们提供了处理
 
     python eval.py --weights=$PATH_TO_WEIGHTS
 
-- 进行评估时，可修改命令行中的`weights`参数指定需要评估的权重，如果不设置，将使用默认参数文件checkpoint/final.pdparams。
+- 进行评估时，可修改命令行中的`weights`参数指定需要评估的权重，若未指定，脚本会下载已发布的模型[model](https://paddlemodels.bj.bcebos.com/hapi/bmn.pdparams)进行评估。
 
-- 上述程序会将运行结果保存在output/EVAL/BMN\_results文件夹下，测试结果保存在evaluate\_results/bmn\_results\_validation.json文件中。
+- 上述程序会将运行结果保存在`--output_path`参数指定的文件夹下，默认为output/EVAL/BMN\_results；测试结果保存在`--result_path`参数指定的文件夹下，默认为evaluate\_results。
 
 - 注：评估时可能会出现loss为nan的情况。这是由于评估时用的是单个样本，可能存在没有iou>0.6的样本，所以为nan，对最终的评估结果没有影响。
 
@@ -87,9 +81,9 @@ BMN的训练数据采用ActivityNet1.3提供的数据集，我们提供了处理
 
 - ActivityNet数据集的具体使用说明可以参考其[官方网站](http://activity-net.org)
 
-- 下载指标评估代码，请从[ActivityNet Gitub repository](https://github.com/activitynet/ActivityNet.git)下载，将Evaluation文件夹拷贝至models/dygraph/bmn目录下。(注：由于第三方评估代码不支持python3，此处建议使用python2进行评估；若使用python3，print函数需要添加括号，请对Evaluation目录下的.py文件做相应修改。)
+- 下载指标评估代码，请从[ActivityNet Gitub repository](https://github.com/activitynet/ActivityNet.git)下载，将Evaluation文件夹拷贝至hapi/examples/bmn目录下。(注：由于第三方评估代码不支持python3，此处建议使用python2进行评估；若使用python3，print函数需要添加括号，请对Evaluation目录下的.py文件做相应修改。)
 
-- 请下载[activity\_net\_1\_3\_new.json](https://paddlemodels.bj.bcebos.com/video_detection/activity_net_1_3_new.json)文件，并将其放置在models/dygraph/bmn/Evaluation/data目录下，相较于原始的activity\_net.v1-3.min.json文件，我们过滤了其中一些失效的视频条目。
+- 请下载[activity\_net\_1\_3\_new.json](https://paddlemodels.bj.bcebos.com/video_detection/activity_net_1_3_new.json)文件，并将其放置在hapi/examples/bmn/Evaluation/data目录下，相较于原始的activity\_net.v1-3.min.json文件，我们过滤了其中一些失效的视频条目。
 
 - 计算精度指标
 
@@ -100,7 +94,7 @@ BMN的训练数据采用ActivityNet1.3提供的数据集，我们提供了处理
 
 | AR@1 | AR@5 | AR@10 | AR@100 | AUC |
 | :---: | :---: | :---: | :---: | :---: |
-| 33.46 | 49.25 | 56.25 | 75.40 | 67.16% |
+| 33.10 | 49.18 | 56.54 | 75.12 | 67.16% |
 
 
 ## 模型推断
@@ -110,9 +104,9 @@ BMN的训练数据采用ActivityNet1.3提供的数据集，我们提供了处理
     python predict.py --weights=$PATH_TO_WEIGHTS \
                       --filelist=$FILELIST
 
-- 使用python命令行启动程序时，`--filelist`参数指定待推断的文件列表，如果不设置，默认为./infer.list。`--weights`参数为训练好的权重参数，如果不设置，将使用默认参数文件checkpoint/final.pdparams。
+- 使用python命令行启动程序时，`--filelist`参数指定待推断的文件列表，如果不设置，默认为./infer.list。`--weights`参数为训练好的权重参数，若未指定，脚本会下载已发布的模型[model](https://paddlemodels.bj.bcebos.com/hapi/bmn.pdparams)进行预测。
 
-- 上述程序会将运行结果保存在output/INFER/BMN\_results文件夹下，测试结果保存在predict\_results/bmn\_results\_test.json文件中。
+- 上述程序会将运行结果保存在`--output_path`参数指定的文件夹下，默认为output/INFER/BMN\_results；测试结果保存在`--result_path`参数指定的文件夹下，默认为predict\_results。
 
 
 ## 参考论文

bmn/bmn.yaml → examples/bmn/bmn.yaml

@@ -12,11 +12,10 @@ MODEL:
 TRAIN:
   subset: "train"
   epoch: 9
-  batch_size: 4
+  batch_size: 4 
   num_workers: 4
   use_shuffle: True
   device: "gpu"
-  num_gpus: 4
   learning_rate: 0.001
   learning_rate_decay: 0.1
   lr_decay_iter: 4200
@@ -29,10 +28,6 @@ TEST:
   subset: "validation"
   batch_size: 1
   num_workers: 1
-  use_buffer: False
-  snms_alpha: 0.001
-  snms_t1: 0.5
-  snms_t2: 0.9
   output_path: "output/EVAL/BMN_results"
   result_path: "evaluate_results"
 
@@ -40,10 +35,6 @@ INFER:
   subset: "test"
   batch_size: 1
   num_workers: 1
-  use_buffer: False
-  snms_alpha: 0.4
-  snms_t1: 0.5
-  snms_t2: 0.9
   filelist: './infer.list'
   output_path: "output/INFER/BMN_results"
   result_path: "predict_results"

bmn/bmn_metric.py → examples/bmn/bmn_metric.py

@@ -20,7 +20,7 @@ import json
 
 sys.path.append('../')
 
-from metrics import Metric
+from hapi.metrics import Metric
 from bmn_utils import boundary_choose, bmn_post_processing
 
 
@@ -36,13 +36,26 @@ class BmnMetric(Metric):
         #get video_dict and video_list
         if self.mode == 'test':
             self.get_test_dataset_dict()
+            if not os.path.isdir(self.cfg.TEST.output_path):
+                os.makedirs(self.cfg.TEST.output_path)
+            if not os.path.isdir(self.cfg.TEST.result_path):
+                os.makedirs(self.cfg.TEST.result_path)
         elif self.mode == 'infer':
             self.get_infer_dataset_dict()
+            if not os.path.isdir(self.cfg.INFER.output_path):
+                os.makedirs(self.cfg.INFER.output_path)
+            if not os.path.isdir(self.cfg.INFER.result_path):
+                os.makedirs(self.cfg.INFER.result_path)
 
-    def add_metric_op(self, preds, label):
-        pred_bm, pred_start, pred_en = preds
-        video_index = label[-1]
-        return [pred_bm, pred_start, pred_en, video_index]  #return list
+    def add_metric_op(self, *args):
+        if self.mode == 'test':
+            # only extract pred_bm, pred_start, pred_en from outputs
+            # and video_index from label here
+            pred_bm, pred_start, pred_en, _, _, _, video_index = args
+        else:
+            # in infer mode, labels only contains video_index
+            pred_bm, pred_start, pred_en, video_index = args
+        return pred_bm, pred_start, pred_en, video_index
 
     def update(self, pred_bm, pred_start, pred_end, fid):
         # generate proposals

bmn/bmn_utils.py → examples/bmn/bmn_utils.py

@@ -162,56 +162,3 @@ def bmn_post_processing(video_dict, subset, output_path, result_path):
     outfile.close()
 
 
-def _get_interp1d_bin_mask(seg_xmin, seg_xmax, tscale, num_sample,
-                           num_sample_perbin):
-    """ generate sample mask for a boundary-matching pair """
-    plen = float(seg_xmax - seg_xmin)
-    plen_sample = plen / (num_sample * num_sample_perbin - 1.0)
-    total_samples = [
-        seg_xmin + plen_sample * ii
-        for ii in range(num_sample * num_sample_perbin)
-    ]
-    p_mask = []
-    for idx in range(num_sample):
-        bin_samples = total_samples[idx * num_sample_perbin:(idx + 1) *
-                                    num_sample_perbin]
-        bin_vector = np.zeros([tscale])
-        for sample in bin_samples:
-            sample_upper = math.ceil(sample)
-            sample_decimal, sample_down = math.modf(sample)
-            if int(sample_down) <= (tscale - 1) and int(sample_down) >= 0:
-                bin_vector[int(sample_down)] += 1 - sample_decimal
-            if int(sample_upper) <= (tscale - 1) and int(sample_upper) >= 0:
-                bin_vector[int(sample_upper)] += sample_decimal
-        bin_vector = 1.0 / num_sample_perbin * bin_vector
-        p_mask.append(bin_vector)
-    p_mask = np.stack(p_mask, axis=1)
-    return p_mask
-
-
-def get_interp1d_mask(tscale, dscale, prop_boundary_ratio, num_sample,
-                      num_sample_perbin):
-    """ generate sample mask for each point in Boundary-Matching Map """
-    mask_mat = []
-    for start_index in range(tscale):
-        mask_mat_vector = []
-        for duration_index in range(dscale):
-            if start_index + duration_index < tscale:
-                p_xmin = start_index
-                p_xmax = start_index + duration_index
-                center_len = float(p_xmax - p_xmin) + 1
-                sample_xmin = p_xmin - center_len * prop_boundary_ratio
-                sample_xmax = p_xmax + center_len * prop_boundary_ratio
-                p_mask = _get_interp1d_bin_mask(sample_xmin, sample_xmax,
-                                                tscale, num_sample,
-                                                num_sample_perbin)
-            else:
-                p_mask = np.zeros([tscale, num_sample])
-            mask_mat_vector.append(p_mask)
-        mask_mat_vector = np.stack(mask_mat_vector, axis=2)
-        mask_mat.append(mask_mat_vector)
-    mask_mat = np.stack(mask_mat, axis=3)
-    mask_mat = mask_mat.astype(np.float32)
-
-    sample_mask = np.reshape(mask_mat, [tscale, -1])
-    return sample_mask

bmn/eval.py → examples/bmn/eval.py

@@ -18,11 +18,10 @@ import sys
 import logging
 import paddle.fluid as fluid
 
-sys.path.append('../')
+from hapi.model import set_device, Input
 
-from model import set_device, Input
+from modeling import bmn, BmnLoss
 from bmn_metric import BmnMetric
-from bmn_model import BMN, BmnLoss
 from reader import BmnDataset
 from config_utils import *
 
@@ -39,7 +38,6 @@ def parse_args():
     parser.add_argument(
         "-d",
         "--dynamic",
-        default=True,
         action='store_true',
         help="enable dygraph mode, only support dynamic mode at present time")
     parser.add_argument(
@@ -55,9 +53,20 @@ def parse_args():
     parser.add_argument(
         '--weights',
         type=str,
-        default="checkpoint/final",
+        default=None,
         help='weight path, None to automatically download weights provided by Paddle.'
     )
+    parser.add_argument(
+        '--output_path',
+        type=str,
+        default="output/EVAL/BMN_results",
+        help='output dir path, default to use output/EVAL/BMN_results')
+    parser.add_argument(
+        '--result_path',
+        type=str,
+        default="evaluate_results/",
+        help='output dir path after post processing, default to use ./evaluate_results/'
+    )
     parser.add_argument(
         '--log_interval',
         type=int,
@@ -69,17 +78,21 @@ def parse_args():
 
 # Performance Evaluation
 def test_bmn(args):
-    # only support dynamic mode at present time
     device = set_device(args.device)
     fluid.enable_dygraph(device) if args.dynamic else None
 
+    #config setting
     config = parse_config(args.config_file)
     eval_cfg = merge_configs(config, 'test', vars(args))
-    if not os.path.isdir(config.TEST.output_path):
-        os.makedirs(config.TEST.output_path)
-    if not os.path.isdir(config.TEST.result_path):
-        os.makedirs(config.TEST.result_path)
 
+    feat_dim = config.MODEL.feat_dim
+    tscale = config.MODEL.tscale
+    dscale = config.MODEL.dscale
+    prop_boundary_ratio = config.MODEL.prop_boundary_ratio
+    num_sample = config.MODEL.num_sample
+    num_sample_perbin = config.MODEL.num_sample_perbin
+
+    #input and video index
     inputs = [
         Input(
             [None, config.MODEL.feat_dim, config.MODEL.tscale],
@@ -99,9 +112,14 @@ def test_bmn(args):
     eval_dataset = BmnDataset(eval_cfg, 'test')
 
     #model
-    model = BMN(config, args.dynamic)
+    model = bmn(tscale,
+                dscale,
+                prop_boundary_ratio,
+                num_sample,
+                num_sample_perbin,
+                pretrained=args.weights is None)
     model.prepare(
-        loss_function=BmnLoss(config),
+        loss_function=BmnLoss(tscale, dscale),
         metrics=BmnMetric(
             config, mode='test'),
         inputs=inputs,
@@ -109,11 +127,11 @@ def test_bmn(args):
         device=device)
 
     #load checkpoint
-    if args.weights:
+    if args.weights is not None:
         assert os.path.exists(args.weights + '.pdparams'), \
             "Given weight dir {} not exist.".format(args.weights)
-    logger.info('load test weights from {}'.format(args.weights))
-    model.load(args.weights)
+        logger.info('load test weights from {}'.format(args.weights))
+        model.load(args.weights)
 
     model.evaluate(
         eval_data=eval_dataset,

bmn/bmn_model.py → examples/bmn/modeling.py

@@ -17,11 +17,73 @@ from paddle.fluid import ParamAttr
 import numpy as np
 import math
 
-from bmn_utils import get_interp1d_mask
-from model import Model, Loss
+from hapi.model import Model, Loss
+from hapi.download import get_weights_path
+
+__all__ = ["BMN", "BmnLoss", "bmn"]
 
 DATATYPE = 'float32'
 
+pretrain_infos = {
+    'bmn': ('https://paddlemodels.bj.bcebos.com/hapi/bmn.pdparams',
+            'aa84e3386e1fbd117fb96fa572feeb94')
+}
+
+
+def _get_interp1d_bin_mask(seg_xmin, seg_xmax, tscale, num_sample,
+                           num_sample_perbin):
+    """ generate sample mask for a boundary-matching pair """
+    plen = float(seg_xmax - seg_xmin)
+    plen_sample = plen / (num_sample * num_sample_perbin - 1.0)
+    total_samples = [
+        seg_xmin + plen_sample * ii
+        for ii in range(num_sample * num_sample_perbin)
+    ]
+    p_mask = []
+    for idx in range(num_sample):
+        bin_samples = total_samples[idx * num_sample_perbin:(idx + 1) *
+                                    num_sample_perbin]
+        bin_vector = np.zeros([tscale])
+        for sample in bin_samples:
+            sample_upper = math.ceil(sample)
+            sample_decimal, sample_down = math.modf(sample)
+            if int(sample_down) <= (tscale - 1) and int(sample_down) >= 0:
+                bin_vector[int(sample_down)] += 1 - sample_decimal
+            if int(sample_upper) <= (tscale - 1) and int(sample_upper) >= 0:
+                bin_vector[int(sample_upper)] += sample_decimal
+        bin_vector = 1.0 / num_sample_perbin * bin_vector
+        p_mask.append(bin_vector)
+    p_mask = np.stack(p_mask, axis=1)
+    return p_mask
+
+
+def get_interp1d_mask(tscale, dscale, prop_boundary_ratio, num_sample,
+                      num_sample_perbin):
+    """ generate sample mask for each point in Boundary-Matching Map """
+    mask_mat = []
+    for start_index in range(tscale):
+        mask_mat_vector = []
+        for duration_index in range(dscale):
+            if start_index + duration_index < tscale:
+                p_xmin = start_index
+                p_xmax = start_index + duration_index
+                center_len = float(p_xmax - p_xmin) + 1
+                sample_xmin = p_xmin - center_len * prop_boundary_ratio
+                sample_xmax = p_xmax + center_len * prop_boundary_ratio
+                p_mask = _get_interp1d_bin_mask(sample_xmin, sample_xmax,
+                                                tscale, num_sample,
+                                                num_sample_perbin)
+            else:
+                p_mask = np.zeros([tscale, num_sample])
+            mask_mat_vector.append(p_mask)
+        mask_mat_vector = np.stack(mask_mat_vector, axis=2)
+        mask_mat.append(mask_mat_vector)
+    mask_mat = np.stack(mask_mat, axis=3)
+    mask_mat = mask_mat.astype(np.float32)
+
+    sample_mask = np.reshape(mask_mat, [tscale, -1])
+    return sample_mask
+
 
 # Net
 class Conv1D(fluid.dygraph.Layer):
@@ -64,16 +126,27 @@ class Conv1D(fluid.dygraph.Layer):
 
 
 class BMN(Model):
-    def __init__(self, cfg, is_dygraph=True):
+    """BMN model from
+    `"BMN: Boundary-Matching Network for Temporal Action Proposal Generation" <https://arxiv.org/abs/1907.09702>`_
+
+    Args:
+        tscale (int): sequence length, default 100.
+        dscale (int): max duration length, default 100.
+        prop_boundary_ratio (float): ratio of expanded temporal region in proposal boundary, default 0.5. 
+        num_sample (int): number of samples betweent starting boundary and ending boundary of each propoasl, default 32.
+        num_sample_perbin (int):  number of selected points in each sample, default 3.
+    """
+
+    def __init__(self, tscale, dscale, prop_boundary_ratio, num_sample,
+                 num_sample_perbin):
         super(BMN, self).__init__()
 
         #init config
-        self.tscale = cfg.MODEL.tscale
-        self.dscale = cfg.MODEL.dscale
-        self.prop_boundary_ratio = cfg.MODEL.prop_boundary_ratio
-        self.num_sample = cfg.MODEL.num_sample
-        self.num_sample_perbin = cfg.MODEL.num_sample_perbin
-        self.is_dygraph = is_dygraph
+        self.tscale = tscale
+        self.dscale = dscale
+        self.prop_boundary_ratio = prop_boundary_ratio
+        self.num_sample = num_sample
+        self.num_sample_perbin = num_sample_perbin
 
         self.hidden_dim_1d = 256
         self.hidden_dim_2d = 128
@@ -124,23 +197,17 @@ class BMN(Model):
             padding=1,
             act="relu")
 
-        # init to speed up
+        # get sample mask 
         sample_mask_array = get_interp1d_mask(
             self.tscale, self.dscale, self.prop_boundary_ratio,
             self.num_sample, self.num_sample_perbin)
-        if self.is_dygraph:
-            self.sample_mask = fluid.dygraph.base.to_variable(
-                sample_mask_array)
-        else:  # static
-            self.sample_mask = fluid.layers.create_parameter(
-                shape=[
-                    self.tscale, self.num_sample * self.dscale * self.tscale
-                ],
-                dtype=DATATYPE,
-                attr=fluid.ParamAttr(
-                    name="sample_mask", trainable=False),
-                default_initializer=fluid.initializer.NumpyArrayInitializer(
-                    sample_mask_array))
+        self.sample_mask = fluid.layers.create_parameter(
+            shape=[self.tscale, self.num_sample * self.dscale * self.tscale],
+            dtype=DATATYPE,
+            attr=fluid.ParamAttr(
+                name="sample_mask", trainable=False),
+            default_initializer=fluid.initializer.NumpyArrayInitializer(
+                sample_mask_array))
 
         self.sample_mask.stop_gradient = True
 
@@ -221,21 +288,30 @@ class BMN(Model):
 
 
 class BmnLoss(Loss):
-    def __init__(self, cfg):
+    """Loss for BMN model
+
+    Args:
+        tscale (int): sequence length, default 100.
+        dscale (int): max duration length, default 100.
+    """
+
+    def __init__(self, tscale, dscale):
         super(BmnLoss, self).__init__()
-        self.cfg = cfg
+        self.tscale = tscale
+        self.dscale = dscale
 
     def _get_mask(self):
-        dscale = self.cfg.MODEL.dscale
-        tscale = self.cfg.MODEL.tscale
         bm_mask = []
-        for idx in range(dscale):
-            mask_vector = [1 for i in range(tscale - idx)
+        for idx in range(self.dscale):
+            mask_vector = [1 for i in range(self.tscale - idx)
                            ] + [0 for i in range(idx)]
             bm_mask.append(mask_vector)
         bm_mask = np.array(bm_mask, dtype=np.float32)
         self_bm_mask = fluid.layers.create_global_var(
-            shape=[dscale, tscale], value=0, dtype=DATATYPE, persistable=True)
+            shape=[self.dscale, self.tscale],
+            value=0,
+            dtype=DATATYPE,
+            persistable=True)
         fluid.layers.assign(bm_mask, self_bm_mask)
         self_bm_mask.stop_gradient = True
         return self_bm_mask
@@ -362,3 +438,29 @@ class BmnLoss(Loss):
 
         loss = tem_loss + 10 * pem_reg_loss + pem_cls_loss
         return loss
+
+
+def bmn(tscale,
+        dscale,
+        prop_boundary_ratio,
+        num_sample,
+        num_sample_perbin,
+        pretrained=True):
+    """BMN model
+    
+    Args:
+        tscale (int): sequence length, default 100.
+        dscale (int): max duration length, default 100.
+        prop_boundary_ratio (float): ratio of expanded temporal region in proposal boundary, default 0.5. 
+        num_sample (int): number of samples betweent starting boundary and ending boundary of each propoasl, default 32.
+        num_sample_perbin (int):  number of selected points in each sample, default 3.
+        pretrained (bool): If True, returns a model with pre-trained model, default True.
+    """
+    model = BMN(tscale, dscale, prop_boundary_ratio, num_sample,
+                num_sample_perbin)
+    if pretrained:
+        weight_path = get_weights_path(*(pretrain_infos['bmn']))
+        assert weight_path.endswith('.pdparams'), \
+                "suffix of weight must be .pdparams"
+        model.load(weight_path)
+    return model

bmn/predict.py → examples/bmn/predict.py

@@ -18,11 +18,10 @@ import os
 import logging
 import paddle.fluid as fluid
 
-sys.path.append('../')
+from hapi.model import set_device, Input
 
-from model import set_device, Input
+from modeling import bmn, BmnLoss
 from bmn_metric import BmnMetric
-from bmn_model import BMN, BmnLoss
 from reader import BmnDataset
 from config_utils import *
 
@@ -39,7 +38,6 @@ def parse_args():
     parser.add_argument(
         "-d",
         "--dynamic",
-        default=True,
         action='store_true',
         help="enable dygraph mode, only support dynamic mode at present time")
     parser.add_argument(
@@ -52,14 +50,25 @@ def parse_args():
     parser.add_argument(
         '--weights',
         type=str,
-        default="checkpoint/final",
+        default=None,
         help='weight path, None to automatically download weights provided by Paddle.'
     )
     parser.add_argument(
-        '--save_dir',
+        '--filelist',
+        type=str,
+        default="infer.list",
+        help='infer file list, default to use ./infer.list')
+    parser.add_argument(
+        '--output_path',
+        type=str,
+        default="output/INFER/BMN_results",
+        help='output dir path, default to use output/INFER/BMN_results')
+    parser.add_argument(
+        '--result_path',
         type=str,
         default="predict_results/",
-        help='output dir path, default to use ./predict_results/')
+        help='output dir path after post processing, default to use ./predict_results/'
+    )
     parser.add_argument(
         '--log_interval',
         type=int,
@@ -71,18 +80,21 @@ def parse_args():
 
 # Prediction
 def infer_bmn(args):
-    # only support dynamic mode at present time
     device = set_device(args.device)
     fluid.enable_dygraph(device) if args.dynamic else None
 
+    #config setting
     config = parse_config(args.config_file)
     infer_cfg = merge_configs(config, 'infer', vars(args))
 
-    if not os.path.isdir(config.INFER.output_path):
-        os.makedirs(config.INFER.output_path)
-    if not os.path.isdir(config.INFER.result_path):
-        os.makedirs(config.INFER.result_path)
+    feat_dim = config.MODEL.feat_dim
+    tscale = config.MODEL.tscale
+    dscale = config.MODEL.dscale
+    prop_boundary_ratio = config.MODEL.prop_boundary_ratio
+    num_sample = config.MODEL.num_sample
+    num_sample_perbin = config.MODEL.num_sample_perbin
 
+    #input and video index
     inputs = [
         Input(
             [None, config.MODEL.feat_dim, config.MODEL.tscale],
@@ -94,7 +106,13 @@ def infer_bmn(args):
     #data
     infer_dataset = BmnDataset(infer_cfg, 'infer')
 
-    model = BMN(config, args.dynamic)
+    #model
+    model = bmn(tscale,
+                dscale,
+                prop_boundary_ratio,
+                num_sample,
+                num_sample_perbin,
+                pretrained=args.weights is None)
     model.prepare(
         metrics=BmnMetric(
             config, mode='infer'),
@@ -103,12 +121,12 @@ def infer_bmn(args):
         device=device)
 
     # load checkpoint
-    if args.weights:
+    if args.weights is not None:
         assert os.path.exists(
             args.weights +
             ".pdparams"), "Given weight dir {} not exist.".format(args.weights)
-    logger.info('load test weights from {}'.format(args.weights))
-    model.load(args.weights)
+        logger.info('load test weights from {}'.format(args.weights))
+        model.load(args.weights)
 
     # here use model.eval instead of model.test, as post process is required in our case
     model.evaluate(

bmn/reader.py → examples/bmn/reader.py

@@ -21,8 +21,8 @@ import sys
 
 sys.path.append('../')
 
-from distributed import DistributedBatchSampler
-from paddle.fluid.io import Dataset, DataLoader
+from hapi.distributed import DistributedBatchSampler
+from paddle.io import Dataset, DataLoader
 
 logger = logging.getLogger(__name__)
 

bmn/run.sh → examples/bmn/run.sh

 export CUDA_VISIBLE_DEVICES=0,1,2,3
-
 python -m paddle.distributed.launch train.py

bmn/train.py → examples/bmn/train.py

@@ -18,12 +18,11 @@ import logging
 import sys
 import os
 
-sys.path.append('../')
+from hapi.model import set_device, Input
 
-from model import set_device, Input
-from bmn_model import BMN, BmnLoss
 from reader import BmnDataset
 from config_utils import *
+from modeling import bmn, BmnLoss
 
 DATATYPE = 'float32'
 
@@ -36,11 +35,7 @@ logger = logging.getLogger(__name__)
 def parse_args():
     parser = argparse.ArgumentParser("Paddle high level api of BMN.")
     parser.add_argument(
-        "-d",
-        "--dynamic",
-        default=True,
-        action='store_true',
-        help="enable dygraph mode")
+        "-d", "--dynamic", action='store_true', help="enable dygraph mode")
     parser.add_argument(
         '--config_file',
         type=str,
@@ -50,7 +45,7 @@ def parse_args():
         '--batch_size',
         type=int,
         default=None,
-        help='training batch size. None to use config file setting.')
+        help='training batch size. None for read from config file.')
     parser.add_argument(
         '--learning_rate',
         type=float,
@@ -70,8 +65,8 @@ def parse_args():
     parser.add_argument(
         '--epoch',
         type=int,
-        default=9,
-        help='epoch number, 0 for read from config file')
+        default=None,
+        help='epoch number, None for read from config file')
     parser.add_argument(
         '--valid_interval',
         type=int,
@@ -115,22 +110,23 @@ def train_bmn(args):
     if not os.path.isdir(args.save_dir):
         os.makedirs(args.save_dir)
 
+    #config setting
     config = parse_config(args.config_file)
     train_cfg = merge_configs(config, 'train', vars(args))
     val_cfg = merge_configs(config, 'valid', vars(args))
 
-    inputs = [
-        Input(
-            [None, config.MODEL.feat_dim, config.MODEL.tscale],
-            'float32',
-            name='feat_input')
-    ]
-    gt_iou_map = Input(
-        [None, config.MODEL.dscale, config.MODEL.tscale],
-        'float32',
-        name='gt_iou_map')
-    gt_start = Input([None, config.MODEL.tscale], 'float32', name='gt_start')
-    gt_end = Input([None, config.MODEL.tscale], 'float32', name='gt_end')
+    feat_dim = config.MODEL.feat_dim
+    tscale = config.MODEL.tscale
+    dscale = config.MODEL.dscale
+    prop_boundary_ratio = config.MODEL.prop_boundary_ratio
+    num_sample = config.MODEL.num_sample
+    num_sample_perbin = config.MODEL.num_sample_perbin
+
+    # input and label list
+    inputs = [Input([None, feat_dim, tscale], 'float32', name='feat_input')]
+    gt_iou_map = Input([None, dscale, tscale], 'float32', name='gt_iou_map')
+    gt_start = Input([None, tscale], 'float32', name='gt_start')
+    gt_end = Input([None, tscale], 'float32', name='gt_end')
     labels = [gt_iou_map, gt_start, gt_end]
 
     # data
@@ -138,11 +134,16 @@ def train_bmn(args):
     val_dataset = BmnDataset(val_cfg, 'valid')
 
     # model
-    model = BMN(config, args.dynamic)
+    model = bmn(tscale,
+                dscale,
+                prop_boundary_ratio,
+                num_sample,
+                num_sample_perbin,
+                pretrained=False)
     optim = optimizer(config, parameter_list=model.parameters())
     model.prepare(
         optimizer=optim,
-        loss_function=BmnLoss(config),
+        loss_function=BmnLoss(tscale, dscale),
         inputs=inputs,
         labels=labels,
         device=device)
@@ -150,11 +151,10 @@ def train_bmn(args):
     # if resume weights is given, load resume weights directly
     if args.resume is not None:
         model.load(args.resume)
-
     model.fit(train_data=train_dataset,
               eval_data=val_dataset,
               batch_size=train_cfg.TRAIN.batch_size,
-              epochs=args.epoch,
+              epochs=train_cfg.TRAIN.epoch,
               eval_freq=args.valid_interval,
               log_freq=args.log_interval,
               save_dir=args.save_dir,

cyclegan/README.md → examples/cyclegan/README.md

@@ -80,12 +80,19 @@ data/cityscapes/testA/412_A.jpg
 
 ### 训练
 
-在GPU单卡上训练:
+在GPU单卡上静态图训练:
 
 ```
-env CUDA_VISIBLE_DEVICES=0 python train.py
+env CUDA_VISIBLE_DEVICES=0 python train.py --checkpoint_path=checkpoint_static
 ```
 
+在GPU单卡上动态图训练:
+
+```
+env CUDA_VISIBLE_DEVICES=0 python train.py --dynamic --checkpoint_path=checkpoint_dynamic
+```
+
+
 执行`python train.py --help`可查看更多使用方式和参数详细说明。
 
 图1为训练152轮的训练损失示意图，其中横坐标轴为训练轮数，纵轴为在训练集上的损失。其中，'g_loss','da_loss'和'db_loss'分别为生成器、判别器A和判别器B的训练损失。

cyclegan/cyclegan.py → examples/cyclegan/cyclegan.py

@@ -18,9 +18,10 @@ from __future__ import print_function
 
 import numpy as np
 
-from layers import ConvBN, DeConvBN
 import paddle.fluid as fluid
-from model import Model, Loss
+from hapi.model import Model, Loss
+
+from layers import ConvBN, DeConvBN
 
 
 class ResnetBlock(fluid.dygraph.Layer):

cyclegan/data.py → examples/cyclegan/data.py

@@ -20,6 +20,8 @@ import random
 import numpy as np
 from PIL import Image, ImageOps
 
+import paddle
+
 DATASET = "cityscapes"
 A_LIST_FILE = "./data/" + DATASET + "/trainA.txt"
 B_LIST_FILE = "./data/" + DATASET + "/trainB.txt"
@@ -27,10 +29,8 @@ A_TEST_LIST_FILE = "./data/" + DATASET + "/testA.txt"
 B_TEST_LIST_FILE = "./data/" + DATASET + "/testB.txt"
 IMAGES_ROOT = "./data/" + DATASET + "/"
 
-import paddle.fluid as fluid
-
 
-class Cityscapes(fluid.io.Dataset):
+class Cityscapes(paddle.io.Dataset):
     def __init__(self, root_path, file_path, mode='train', return_name=False):
         self.root_path = root_path
         self.file_path = file_path

cyclegan/infer.py → examples/cyclegan/infer.py

@@ -25,9 +25,9 @@ from PIL import Image
 from scipy.misc import imsave
 
 import paddle.fluid as fluid
-from check import check_gpu, check_version
+from hapi.model import Model, Input, set_device
 
-from model import Model, Input, set_device
+from check import check_gpu, check_version
 from cyclegan import Generator, GeneratorCombine
 
 
@@ -43,7 +43,7 @@ def main():
     im_shape = [-1, 3, 256, 256]
     input_A = Input(im_shape, 'float32', 'input_A')
     input_B = Input(im_shape, 'float32', 'input_B')
-    g.prepare(inputs=[input_A, input_B])
+    g.prepare(inputs=[input_A, input_B], device=FLAGS.device)
     g.load(FLAGS.init_model, skip_mismatch=True, reset_optimizer=True)
 
     out_path = FLAGS.output + "/single"
@@ -59,10 +59,10 @@ def main():
         data = image.transpose([2, 0, 1])[np.newaxis, :]
 
         if FLAGS.input_style == "A":
-            _, fake, _, _ = g.test([data, data])
+            _, fake, _, _ = g.test_batch([data, data])
 
         if FLAGS.input_style == "B":
-            fake, _, _, _ = g.test([data, data])
+            fake, _, _, _ = g.test_batch([data, data])
 
         fake = np.squeeze(fake[0]).transpose([1, 2, 0])
 
@@ -74,7 +74,7 @@ def main():
 if __name__ == "__main__":
     parser = argparse.ArgumentParser("CycleGAN inference")
     parser.add_argument(
-        "-d", "--dynamic", action='store_false', help="Enable dygraph mode")
+        "-d", "--dynamic", action='store_true', help="Enable dygraph mode")
     parser.add_argument(
         "-p",
         "--device",

cyclegan/test.py → examples/cyclegan/test.py

@@ -22,9 +22,9 @@ import numpy as np
 from scipy.misc import imsave
 
 import paddle.fluid as fluid
-from check import check_gpu, check_version
+from hapi.model import Model, Input, set_device
 
-from model import Model, Input, set_device
+from check import check_gpu, check_version
 from cyclegan import Generator, GeneratorCombine
 import data as data
 
@@ -41,7 +41,7 @@ def main():
     im_shape = [-1, 3, 256, 256]
     input_A = Input(im_shape, 'float32', 'input_A')
     input_B = Input(im_shape, 'float32', 'input_B')
-    g.prepare(inputs=[input_A, input_B])
+    g.prepare(inputs=[input_A, input_B], device=FLAGS.device)
     g.load(FLAGS.init_model, skip_mismatch=True, reset_optimizer=True)
 
     if not os.path.exists(FLAGS.output):
@@ -56,7 +56,7 @@ def main():
         data_A = np.array(data_A).astype("float32")
         data_B = np.array(data_B).astype("float32")
 
-        fake_A, fake_B, cyc_A, cyc_B = g.test([data_A, data_B])
+        fake_A, fake_B, cyc_A, cyc_B = g.test_batch([data_A, data_B])
 
         datas = [fake_A, fake_B, cyc_A, cyc_B, data_A, data_B]
         odatas = []
@@ -75,7 +75,7 @@ def main():
 if __name__ == "__main__":
     parser = argparse.ArgumentParser("CycleGAN test")
     parser.add_argument(
-        "-d", "--dynamic", action='store_false', help="Enable dygraph mode")
+        "-d", "--dynamic", action='store_true', help="Enable dygraph mode")
     parser.add_argument(
         "-p",
         "--device",

cyclegan/train.py → examples/cyclegan/train.py

@@ -24,12 +24,11 @@ import time
 
 import paddle
 import paddle.fluid as fluid
-from check import check_gpu, check_version
-
-from model import Model, Input, set_device
+from hapi.model import Model, Input, set_device
 
-import data as data
+from check import check_gpu, check_version
 from cyclegan import Generator, Discriminator, GeneratorCombine, GLoss, DLoss
+import data as data
 
 step_per_epoch = 2974
 
@@ -76,23 +75,26 @@ def main():
     fake_A = Input(im_shape, 'float32', 'fake_A')
     fake_B = Input(im_shape, 'float32', 'fake_B')
 
-    g_AB.prepare(inputs=[input_A])
-    g_BA.prepare(inputs=[input_B])
+    g_AB.prepare(inputs=[input_A], device=FLAGS.device)
+    g_BA.prepare(inputs=[input_B], device=FLAGS.device)
 
-    g.prepare(g_optimizer, GLoss(), inputs=[input_A, input_B])
-    d_A.prepare(da_optimizer, DLoss(), inputs=[input_B, fake_B])
-    d_B.prepare(db_optimizer, DLoss(), inputs=[input_A, fake_A])
+    g.prepare(g_optimizer, GLoss(), inputs=[input_A, input_B],
+        device=FLAGS.device)
+    d_A.prepare(da_optimizer, DLoss(), inputs=[input_B, fake_B],
+        device=FLAGS.device)
+    d_B.prepare(db_optimizer, DLoss(), inputs=[input_A, fake_A],
+        device=FLAGS.device)
 
     if FLAGS.resume:
         g.load(FLAGS.resume)
 
-    loader_A = fluid.io.DataLoader(
+    loader_A = paddle.io.DataLoader(
         data.DataA(),
         places=place,
         shuffle=True,
         return_list=True,
         batch_size=FLAGS.batch_size)
-    loader_B = fluid.io.DataLoader(
+    loader_B = paddle.io.DataLoader(
         data.DataB(),
         places=place,
         shuffle=True,
@@ -108,14 +110,14 @@ def main():
             data_B = data_B[0][0] if not FLAGS.dynamic else data_B[0]
             start = time.time()
 
-            fake_B = g_AB.test(data_A)[0]
-            fake_A = g_BA.test(data_B)[0]
-            g_loss = g.train([data_A, data_B])[0]
+            fake_B = g_AB.test_batch(data_A)[0]
+            fake_A = g_BA.test_batch(data_B)[0]
+            g_loss = g.train_batch([data_A, data_B])[0]
             fake_pb = B_pool.get(fake_B)
-            da_loss = d_A.train([data_B, fake_pb])[0]
+            da_loss = d_A.train_batch([data_B, fake_pb])[0]
 
             fake_pa = A_pool.get(fake_A)
-            db_loss = d_B.train([data_A, fake_pa])[0]
+            db_loss = d_B.train_batch([data_A, fake_pa])[0]
 
             t = time.time() - start
             if i % 20 == 0:
@@ -128,7 +130,7 @@ def main():
 if __name__ == "__main__":
     parser = argparse.ArgumentParser("CycleGAN Training on Cityscapes")
     parser.add_argument(
-        "-d", "--dynamic", action='store_false', help="Enable dygraph mode")
+        "-d", "--dynamic", action='store_true', help="Enable dygraph mode")
     parser.add_argument(
         "-p",
         "--device",

image_classification/README.MD → examples/image_classification/README.MD

@@ -43,13 +43,13 @@ CUDA_VISIBLE_DEVICES=0,1,2,3 python -m paddle.distributed.launch main.py --arch
 ### 单卡预测
 执行如下命令进行预测
 ```bash
-python -u main.py --arch resnet50 -d --evaly-only /path/to/imagenet 
+python -u main.py --arch resnet50 -d --eval-only /path/to/imagenet 
 ```
 
 ### 多卡预测
 执行如下命令进行多卡预测
 ```bash
-CUDA_VISIBLE_DEVICES=0,1,2,3 python -m paddle.distributed.launch main.py --arch resnet50 --evaly-only /path/to/imagenet
+CUDA_VISIBLE_DEVICES=0,1,2,3 python -m paddle.distributed.launch main.py --arch resnet50 --eval-only /path/to/imagenet
 ```
 
 
@@ -71,15 +71,20 @@ CUDA_VISIBLE_DEVICES=0,1,2,3 python -m paddle.distributed.launch main.py --arch
 * **weight-decay**: 模型权重正则化系数，默认值：1e-4
 * **momentum**: SGD优化器的动量，默认值：0.9
 
+注意：使用```--resume```恢复训练时，假如你的模型路径为```./output/118.pdparams```，你输入的路径不需要带后缀，即```--resume ./output/118```即可。
 
 ## 模型
 
 | 模型 | top1 acc | top5 acc |
 | --- | --- | --- |
-| [ResNet50](https://paddle-hapi.bj.bcebos.com/models/resnet50.pdparams) | 76.28 | 93.04 |
-| [vgg16](https://paddle-hapi.bj.bcebos.com/models/vgg16.pdparams) | 71.84 | 90.71 | 
-| [mobilenet_v1](https://paddle-hapi.bj.bcebos.com/models/mobilenet_v1_x1.0.pdparams) | 71.25 | 89.92 | 
-| [mobilenet_v2](https://paddle-hapi.bj.bcebos.com/models/mobilenet_v2_x1.0.pdparams) | 72.27 | 90.66 | 
+| [ResNet18](https://paddle-hapi.bj.bcebos.com/models/resnet18.pdparams) | 71.72 | 90.60 |
+| [ResNet34](https://paddle-hapi.bj.bcebos.com/models/resnet34.pdparams) | 75.02 | 92.31 |
+| [ResNet50](https://paddle-hapi.bj.bcebos.com/models/resnet50.pdparams) | 76.27 | 93.03 |
+| [ResNet101](https://paddle-hapi.bj.bcebos.com/models/resnet101.pdparams) | 78.33 | 94.04 |
+| [ResNet152](https://paddle-hapi.bj.bcebos.com/models/resnet152.pdparams) | 78.78 | 94.40 |
+| [vgg16](https://paddle-hapi.bj.bcebos.com/models/vgg16.pdparams) | 71.92 | 90.65 | 
+| [mobilenet_v1](https://paddle-hapi.bj.bcebos.com/models/mobilenet_v1_x1.0.pdparams) | 71.16 | 89.89 | 
+| [mobilenet_v2](https://paddle-hapi.bj.bcebos.com/models/mobilenet_v2_x1.0.pdparams) | 72.30 | 90.74 | 
 
 上述模型的复现参数请参考scripts下的脚本。
 

image_classification/imagenet_dataset.py → examples/image_classification/imagenet_dataset.py

@@ -18,81 +18,35 @@ import math
 import random
 import numpy as np
 
-from datasets.folder import DatasetFolder
-
-
-def center_crop_resize(img):
-    h, w = img.shape[:2]
-    c = int(224 / 256 * min((h, w)))
-    i = (h + 1 - c) // 2
-    j = (w + 1 - c) // 2
-    img = img[i:i + c, j:j + c, :]
-    return cv2.resize(img, (224, 224), 0, 0, cv2.INTER_LINEAR)
-
-
-def random_crop_resize(img):
-    height, width = img.shape[:2]
-    area = height * width
-
-    for attempt in range(10):
-        target_area = random.uniform(0.08, 1.) * area
-        log_ratio = (math.log(3 / 4), math.log(4 / 3))
-        aspect_ratio = math.exp(random.uniform(*log_ratio))
-
-        w = int(round(math.sqrt(target_area * aspect_ratio)))
-        h = int(round(math.sqrt(target_area / aspect_ratio)))
-
-        if w <= width and h <= height:
-            i = random.randint(0, height - h)
-            j = random.randint(0, width - w)
-            img = img[i:i + h, j:j + w, :]
-            return cv2.resize(img, (224, 224), 0, 0, cv2.INTER_LINEAR)
-
-    return center_crop_resize(img)
-
-
-def random_flip(img):
-    if np.random.randint(0, 2) == 1:
-        img = img[:, ::-1, :]
-    return img
-
-
-def normalize_permute(img):
-    # transpose and convert to RGB from BGR
-    img = img.astype(np.float32).transpose((2, 0, 1))[::-1, ...]
-    mean = np.array([123.675, 116.28, 103.53], dtype=np.float32)
-    std = np.array([58.395, 57.120, 57.375], dtype=np.float32)
-    invstd = 1. / std
-    for v, m, s in zip(img, mean, invstd):
-        v.__isub__(m).__imul__(s)
-    return img
-
-
-def compose(functions):
-    def process(sample):
-        img, label = sample
-        for fn in functions:
-            img = fn(img)
-        return img, label
-
-    return process
+from hapi.datasets import DatasetFolder
+from hapi.vision.transforms import transforms
+from paddle import fluid
 
 
 class ImageNetDataset(DatasetFolder):
     def __init__(self, path, mode='train'):
         super(ImageNetDataset, self).__init__(path)
         self.mode = mode
+
+        normalize = transforms.Normalize(
+            mean=[123.675, 116.28, 103.53], std=[58.395, 57.120, 57.375])
         if self.mode == 'train':
-            self.transform = compose([
-                cv2.imread, random_crop_resize, random_flip, normalize_permute
+            self.transform = transforms.Compose([
+                transforms.RandomResizedCrop(224),
+                transforms.RandomHorizontalFlip(),
+                transforms.Permute(mode='CHW'), normalize
             ])
         else:
-            self.transform = compose(
-                [cv2.imread, center_crop_resize, normalize_permute])
+            self.transform = transforms.Compose([
+                transforms.Resize(256), transforms.CenterCrop(224),
+                transforms.Permute(mode='CHW'), normalize
+            ])
 
     def __getitem__(self, idx):
-        img, label = self.samples[idx]
-        return self.transform((img, [label]))
+        img_path, label = self.samples[idx]
+        img = cv2.imread(img_path).astype(np.float32)
+        label = np.array([label])
+        return self.transform(img, label)
 
     def __len__(self):
         return len(self.samples)

image_classification/main.py → examples/image_classification/main.py

@@ -24,15 +24,17 @@ sys.path.append('../')
 import time
 import math
 import numpy as np
-import models
+
 import paddle.fluid as fluid
+from paddle.fluid.dygraph.parallel import ParallelEnv
+from paddle.io import BatchSampler, DataLoader
+
+from hapi.model import CrossEntropy, Input, set_device
+from hapi.distributed import DistributedBatchSampler
+from hapi.metrics import Accuracy
+import hapi.vision.models as models
 
-from model import CrossEntropy, Input, set_device
 from imagenet_dataset import ImageNetDataset
-from distributed import DistributedBatchSampler
-from paddle.fluid.dygraph.parallel import ParallelEnv
-from metrics import Accuracy
-from paddle.fluid.io import BatchSampler, DataLoader
 
 
 def make_optimizer(step_per_epoch, parameter_list=None):
@@ -74,6 +76,9 @@ def main():
     device = set_device(FLAGS.device)
     fluid.enable_dygraph(device) if FLAGS.dynamic else None
 
+    model_list = [x for x in models.__dict__["__all__"]]
+    assert FLAGS.arch in model_list, "Expected FLAGS.arch in {}, but received {}".format(
+        model_list, FLAGS.arch)
     model = models.__dict__[FLAGS.arch](pretrained=FLAGS.eval_only and
                                         not FLAGS.resume)
 
@@ -92,7 +97,13 @@ def main():
             len(train_dataset) * 1. / FLAGS.batch_size / ParallelEnv().nranks),
         parameter_list=model.parameters())
 
-    model.prepare(optim, CrossEntropy(), Accuracy(topk=(1, 5)), inputs, labels)
+    model.prepare(
+        optim,
+        CrossEntropy(),
+        Accuracy(topk=(1, 5)),
+        inputs,
+        labels,
+        FLAGS.device)
 
     if FLAGS.eval_only:
         model.evaluate(
@@ -150,7 +161,7 @@ if __name__ == '__main__':
         type=str,
         help="checkpoint path to resume")
     parser.add_argument(
-        "--eval-only", action='store_true', help="enable dygraph mode")
+        "--eval-only", action='store_true', help="only evaluate the model")
     parser.add_argument(
         "--lr-scheduler",
         default='piecewise',

examples/image_classification/scripts/mobilenet_v1_x1.0.sh

+export CUDA_VISIBLE_DEVICES=0,1,2,3 
+
+# 默认imagenet数据存储在data/ILSVRC2012/下，去除-d便使用静态图模式运行
+python -m paddle.distributed.launch main.py \
+        --arch mobilenet_v1 \
+        --epoch 120 \
+        --batch-size 64 \
+        --learning-rate 0.1 \
+        --lr-scheduler piecewise \
+        --milestones 30 60 90 \
+        --weight-decay 3e-5 \
+        -d \
+        data/ILSVRC2012/
\ No newline at end of file

examples/image_classification/scripts/mobilenet_v2_x1.0.sh

+export CUDA_VISIBLE_DEVICES=0,1,2,3 
+
+# 默认imagenet数据存储在data/ILSVRC2012/下，去除-d便使用静态图模式运行
+python -m paddle.distributed.launch main.py \
+        --arch mobilenet_v2 \
+        --epoch 240 \
+        --batch-size 64 \
+        --learning-rate 0.1 \
+        --lr-scheduler cosine \
+        --weight-decay 4e-5 \
+        -d \
+        data/ILSVRC2012/
\ No newline at end of file

examples/image_classification/scripts/resnet101.sh

+export CUDA_VISIBLE_DEVICES=0,1,2,3 
+
+# 默认imagenet数据存储在data/ILSVRC2012/下，去除-d便使用静态图模式运行
+python -m paddle.distributed.launch main.py \
+        --arch resnet101 \
+        --epoch 90 \
+        --batch-size 64 \
+        --learning-rate 0.1 \
+        -d \
+        data/ILSVRC2012/
\ No newline at end of file

examples/image_classification/scripts/resnet152.sh

+export CUDA_VISIBLE_DEVICES=0,1,2,3 
+
+# 默认imagenet数据存储在data/ILSVRC2012/下，去除-d便使用静态图模式运行
+python -m paddle.distributed.launch main.py \
+        --arch resnet152 \
+        --epoch 90 \
+        --batch-size 64 \
+        --learning-rate 0.1 \
+        -d \
+        data/ILSVRC2012/
\ No newline at end of file

examples/image_classification/scripts/resnet18.sh

+export CUDA_VISIBLE_DEVICES=0,1,2,3 
+
+# 默认imagenet数据存储在data/ILSVRC2012/下，去除-d便使用静态图模式运行
+python -m paddle.distributed.launch main.py \
+        --arch resnet18 \
+        --epoch 120 \
+        --batch-size 64 \
+        --learning-rate 0.1 \
+        --lr-scheduler cosine \
+        -d \
+        data/ILSVRC2012/
\ No newline at end of file

examples/image_classification/scripts/resnet34.sh

+export CUDA_VISIBLE_DEVICES=0,1,2,3 
+
+# 默认imagenet数据存储在data/ILSVRC2012/下，去除-d便使用静态图模式运行
+python -m paddle.distributed.launch main.py \
+        --arch resnet34 \
+        --epoch 120 \
+        --batch-size 64 \
+        --learning-rate 0.1 \
+        --lr-scheduler cosine \
+        -d \
+        data/ILSVRC2012/
\ No newline at end of file

examples/image_classification/scripts/resnet50.sh

+export CUDA_VISIBLE_DEVICES=0,1,2,3 
+
+# 默认imagenet数据存储在data/ILSVRC2012/下，去除-d便使用静态图模式运行
+python -m paddle.distributed.launch main.py \
+        --arch resnet50 \
+        --epoch 90 \
+        --batch-size 64 \
+        --learning-rate 0.1 \
+        -d \
+        data/ILSVRC2012/
\ No newline at end of file

examples/image_classification/scripts/vgg16.sh

+export CUDA_VISIBLE_DEVICES=0,1,2,3 
+
+# 默认imagenet数据存储在data/ILSVRC2012/下，去除-d便使用静态图模式运行
+python -m paddle.distributed.launch main.py \
+        --arch vgg16 \
+        --epoch 90 \
+        --batch-size 64 \
+        --learning-rate 0.01 \
+        --lr-scheduler cosine \
+        -d \
+        data/ILSVRC2012/
\ No newline at end of file

examples/ocr/README.md

+简介
+--------
+本OCR任务是识别图片单行的字母信息，基于attention的seq2seq结构。 运行本目录下的程序示例需要使用PaddlePaddle develop最新版本。
+
+## 代码结构
+```
+.
+|-- data.py          # 数据读取
+|-- eval.py          # 评估脚本
+|-- images           # 测试图片
+|-- predict.py       # 预测脚本
+|-- seq2seq_attn.py  # 模型
+|-- train.py         # 训练脚本
+`-- utility.py       # 公共模块
+```
+
+## 训练/评估/预测流程
+
+- 设置GPU环境:
+
+```
+export CUDA_VISIBLE_DEVICES=0
+```
+
+- 训练
+
+```
+python train.py
+```
+
+更多参数可以通过`--help`查看。
+
+
+- 动静切换
+
+
+```
+python train.py --dynamic=True
+```
+
+
+- 评估
+
+```
+python eval.py --init_model=checkpoint/final
+```
+
+
+- 预测
+
+目前不支持动态图预测
+
+```
+python predict.py --init_model=checkpoint/final --image_path=images/ --dynamic=False --beam_size=3
+```
+
+预测结果如下:
+
+```
+Image 1: images/112_chubbiness_13557.jpg
+0: chubbines
+1: chubbiness
+2: chubbinesS
+Image 2: images/177_Interfiled_40185.jpg
+0: Interflied
+1: Interfiled
+2: InterfIled
+Image 3: images/325_dame_19109.jpg
+0: da
+1: damo
+2: dame
+Image 4: images/368_fixtures_29232.jpg
+0: firtures
+1: Firtures
+2: fixtures
+```

examples/ocr/data.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+from os import path
+import random
+import traceback
+import copy
+import math
+import tarfile
+from PIL import Image
+
+import logging
+logger = logging.getLogger(__name__)
+
+import paddle
+from paddle import fluid
+from paddle.fluid.dygraph.parallel import ParallelEnv
+
+DATA_MD5 = "7256b1d5420d8c3e74815196e58cdad5"
+DATA_URL = "http://paddle-ocr-data.bj.bcebos.com/data.tar.gz"
+CACHE_DIR_NAME = "attention_data"
+SAVED_FILE_NAME = "data.tar.gz"
+DATA_DIR_NAME = "data"
+TRAIN_DATA_DIR_NAME = "train_images"
+TEST_DATA_DIR_NAME = "test_images"
+TRAIN_LIST_FILE_NAME = "train.list"
+TEST_LIST_FILE_NAME = "test.list"
+
+
+class Resize(object):
+    def __init__(self, height=48):
+        self.interp = Image.NEAREST  # Image.ANTIALIAS
+        self.height = height
+
+    def __call__(self, samples):
+        shape = samples[0][0].size
+        for i in range(len(samples)):
+            im = samples[i][0]
+            im = im.resize((shape[0], self.height), self.interp)
+            samples[i][0] = im
+        return samples
+
+
+class Normalize(object):
+    def __init__(self,
+                 mean=[127.5],
+                 std=[1.0],
+                 scale=False,
+                 channel_first=True):
+        self.mean = mean
+        self.std = std
+        self.scale = scale
+        self.channel_first = channel_first
+        if not (isinstance(self.mean, list) and isinstance(self.std, list) and
+                isinstance(self.scale, bool)):
+            raise TypeError("{}: input type is invalid.".format(self))
+
+    def __call__(self, samples):
+        for i in range(len(samples)):
+            im = samples[i][0]
+            im = np.array(im).astype(np.float32, copy=False)
+            im = im[np.newaxis, ...]
+            mean = np.array(self.mean)[np.newaxis, np.newaxis, :]
+            std = np.array(self.std)[np.newaxis, np.newaxis, :]
+            if self.scale:
+                im = im / 255.0
+            #im -= mean
+            im -= 127.5
+            #im /= std
+            samples[i][0] = im
+        return samples
+
+
+class PadTarget(object):
+    def __init__(self, SOS=0, EOS=1):
+        self.SOS = SOS
+        self.EOS = EOS
+
+    def __call__(self, samples):
+        lens = np.array([len(s[1]) for s in samples], dtype="int64")
+        max_len = np.max(lens)
+        for i in range(len(samples)):
+            label = samples[i][1]
+            if max_len > len(label):
+                pad_label = label + [self.EOS] * (max_len - len(label))
+            else:
+                pad_label = label
+            samples[i][1] = np.array([self.SOS] + pad_label, dtype='int64')
+            # label_out
+            samples[i].append(np.array(pad_label + [self.EOS], dtype='int64'))
+            mask = np.zeros((max_len + 1)).astype('float32')
+            mask[:len(label) + 1] = 1.0
+            # mask
+            samples[i].append(np.array(mask, dtype='float32'))
+        return samples
+
+
+class BatchSampler(fluid.io.BatchSampler):
+    def __init__(self,
+                 dataset,
+                 batch_size,
+                 shuffle=False,
+                 drop_last=True,
+                 seed=None):
+        self._dataset = dataset
+        self._batch_size = batch_size
+        self._shuffle = shuffle
+        self._drop_last = drop_last
+        self._random = np.random
+        self._random.seed(seed)
+        self._nranks = ParallelEnv().nranks
+        self._local_rank = ParallelEnv().local_rank
+        self._device_id = ParallelEnv().dev_id
+        self._num_samples = int(
+            math.ceil(len(self._dataset) * 1.0 / self._nranks))
+        self._total_size = self._num_samples * self._nranks
+        self._epoch = 0
+
+    def __iter__(self):
+        infos = copy.copy(self._dataset._sample_infos)
+        skip_num = 0
+        if self._shuffle:
+            if self._batch_size == 1:
+                self._random.RandomState(self._epoch).shuffle(infos)
+            else:  # partial shuffle
+                infos = sorted(infos, key=lambda x: x.w)
+                skip_num = random.randint(1, 100)
+
+        infos = infos[skip_num:] + infos[:skip_num]
+        infos += infos[:(self._total_size - len(infos))]
+        last_size = self._total_size % (self._batch_size * self._nranks)
+        batches = []
+        for i in range(self._local_rank * self._batch_size,
+                       len(infos) - last_size,
+                       self._batch_size * self._nranks):
+            batches.append(infos[i:i + self._batch_size])
+
+        if (not self._drop_last) and last_size != 0:
+            last_local_size = last_size // self._nranks
+            last_infos = infos[len(infos) - last_size:]
+            start = self._local_rank * last_local_size
+            batches.append(last_infos[start:start + last_local_size])
+
+        if self._shuffle:
+            self._random.RandomState(self._epoch).shuffle(batches)
+            self._epoch += 1
+
+        for batch in batches:
+            batch_indices = [info.idx for info in batch]
+            yield batch_indices
+
+    def __len__(self):
+        if self._drop_last:
+            return self._total_size // self._batch_size
+        else:
+            return math.ceil(self._total_size / float(self._batch_size))
+
+
+class SampleInfo(object):
+    def __init__(self, idx, h, w, im_name, labels):
+        self.idx = idx
+        self.h = h
+        self.w = w
+        self.im_name = im_name
+        self.labels = labels
+
+
+class OCRDataset(paddle.io.Dataset):
+    def __init__(self, image_dir, anno_file):
+        self.image_dir = image_dir
+        self.anno_file = anno_file
+        self._sample_infos = []
+        with open(anno_file, 'r') as f:
+            for i, line in enumerate(f):
+                w, h, im_name, labels = line.strip().split(' ')
+                h, w = int(h), int(w)
+                labels = [int(c) for c in labels.split(',')]
+                self._sample_infos.append(SampleInfo(i, h, w, im_name, labels))
+
+    def __getitem__(self, idx):
+        info = self._sample_infos[idx]
+        im_name, labels = info.im_name, info.labels
+        image = Image.open(path.join(self.image_dir, im_name)).convert('L')
+        return [image, labels]
+
+    def __len__(self):
+        return len(self._sample_infos)
+
+
+def train(
+        root_dir=None,
+        images_dir=None,
+        anno_file=None,
+        shuffle=True, ):
+    if root_dir is None:
+        root_dir = download_data()
+    if images_dir is None:
+        images_dir = TRAIN_DATA_DIR_NAME
+    images_dir = path.join(root_dir, TRAIN_DATA_DIR_NAME)
+    if anno_file is None:
+        anno_file = TRAIN_LIST_FILE_NAME
+    anno_file = path.join(root_dir, TRAIN_LIST_FILE_NAME)
+    return OCRDataset(images_dir, anno_file)
+
+
+def test(
+        root_dir=None,
+        images_dir=None,
+        anno_file=None,
+        shuffle=True, ):
+    if root_dir is None:
+        root_dir = download_data()
+    if images_dir is None:
+        images_dir = TEST_DATA_DIR_NAME
+    images_dir = path.join(root_dir, TEST_DATA_DIR_NAME)
+    if anno_file is None:
+        anno_file = TEST_LIST_FILE_NAME
+    anno_file = path.join(root_dir, TEST_LIST_FILE_NAME)
+    return OCRDataset(images_dir, anno_file)
+
+
+def download_data():
+    '''Download train and test data.
+    '''
+    tar_file = paddle.dataset.common.download(
+        DATA_URL, CACHE_DIR_NAME, DATA_MD5, save_name=SAVED_FILE_NAME)
+    data_dir = path.join(path.dirname(tar_file), DATA_DIR_NAME)
+    if not path.isdir(data_dir):
+        t = tarfile.open(tar_file, "r:gz")
+        t.extractall(path=path.dirname(tar_file))
+        t.close()
+    return data_dir

examples/ocr/eval.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import print_function
+
+import argparse
+import functools
+
+import paddle.fluid.profiler as profiler
+import paddle.fluid as fluid
+
+from hapi.model import Input, set_device
+from hapi.vision.transforms import BatchCompose
+
+from utility import add_arguments, print_arguments
+from utility import SeqAccuracy, LoggerCallBack, SeqBeamAccuracy
+from utility import postprocess
+from seq2seq_attn import Seq2SeqAttModel, Seq2SeqAttInferModel, WeightCrossEntropy
+import data
+
+parser = argparse.ArgumentParser(description=__doc__)
+add_arg = functools.partial(add_arguments, argparser=parser)
+# yapf: disable
+add_arg('batch_size',        int,   32,                 "Minibatch size.")
+add_arg('test_images',       str,   None,               "The directory of images to be used for test.")
+add_arg('test_list',         str,   None,               "The list file of images to be used for training.")
+add_arg('init_model',        str,   'checkpoint/final', "The init model file of directory.")
+add_arg('use_gpu',           bool,  True,               "Whether use GPU to train.")
+add_arg('encoder_size',      int,   200,                "Encoder size.")
+add_arg('decoder_size',      int,   128,                "Decoder size.")
+add_arg('embedding_dim',     int,   128,                "Word vector dim.")
+add_arg('num_classes',       int,   95,                 "Number classes.")
+add_arg('beam_size',         int,   0,                  "If set beam size, will use beam search.")
+add_arg('dynamic',           bool,  False,              "Whether to use dygraph.")
+# yapf: enable
+
+
+def main(FLAGS):
+    device = set_device("gpu" if FLAGS.use_gpu else "cpu")
+    fluid.enable_dygraph(device) if FLAGS.dynamic else None
+    model = Seq2SeqAttModel(
+        encoder_size=FLAGS.encoder_size,
+        decoder_size=FLAGS.decoder_size,
+        emb_dim=FLAGS.embedding_dim,
+        num_classes=FLAGS.num_classes)
+
+    # yapf: disable
+    inputs = [
+        Input([None, 1, 48, 384], "float32", name="pixel"),
+        Input([None, None], "int64", name="label_in")
+    ]
+    labels = [
+        Input([None, None], "int64", name="label_out"),
+        Input([None, None], "float32", name="mask")
+    ]
+    # yapf: enable
+
+    model.prepare(
+        loss_function=WeightCrossEntropy(),
+        metrics=SeqAccuracy(),
+        inputs=inputs,
+        labels=labels,
+        device=device)
+    model.load(FLAGS.init_model)
+
+    test_dataset = data.test()
+    test_collate_fn = BatchCompose(
+        [data.Resize(), data.Normalize(), data.PadTarget()])
+    test_sampler = data.BatchSampler(
+        test_dataset,
+        batch_size=FLAGS.batch_size,
+        drop_last=False,
+        shuffle=False)
+    test_loader = fluid.io.DataLoader(
+        test_dataset,
+        batch_sampler=test_sampler,
+        places=device,
+        num_workers=0,
+        return_list=True,
+        collate_fn=test_collate_fn)
+
+    model.evaluate(
+        eval_data=test_loader,
+        callbacks=[LoggerCallBack(10, 2, FLAGS.batch_size)])
+
+
+def beam_search(FLAGS):
+    device = set_device("gpu" if FLAGS.use_gpu else "cpu")
+    fluid.enable_dygraph(device) if FLAGS.dynamic else None
+    model = Seq2SeqAttInferModel(
+        encoder_size=FLAGS.encoder_size,
+        decoder_size=FLAGS.decoder_size,
+        emb_dim=FLAGS.embedding_dim,
+        num_classes=FLAGS.num_classes,
+        beam_size=FLAGS.beam_size)
+
+    inputs = [
+        Input(
+            [None, 1, 48, 384], "float32", name="pixel"), Input(
+                [None, None], "int64", name="label_in")
+    ]
+    labels = [
+        Input(
+            [None, None], "int64", name="label_out"), Input(
+                [None, None], "float32", name="mask")
+    ]
+    model.prepare(
+        loss_function=None,
+        metrics=SeqBeamAccuracy(),
+        inputs=inputs,
+        labels=labels,
+        device=device)
+    model.load(FLAGS.init_model)
+
+    test_dataset = data.test()
+    test_collate_fn = BatchCompose(
+        [data.Resize(), data.Normalize(), data.PadTarget()])
+    test_sampler = data.BatchSampler(
+        test_dataset,
+        batch_size=FLAGS.batch_size,
+        drop_last=False,
+        shuffle=False)
+    test_loader = fluid.io.DataLoader(
+        test_dataset,
+        batch_sampler=test_sampler,
+        places=device,
+        num_workers=0,
+        return_list=True,
+        collate_fn=test_collate_fn)
+
+    model.evaluate(
+        eval_data=test_loader,
+        callbacks=[LoggerCallBack(10, 2, FLAGS.batch_size)])
+
+
+if __name__ == '__main__':
+    FLAGS = parser.parse_args()
+    print_arguments(FLAGS)
+    if FLAGS.beam_size:
+        beam_search(FLAGS)
+    else:
+        main(FLAGS)

examples/ocr/predict.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import print_function
+
+import os
+import sys
+import random
+import numpy as np
+
+import argparse
+import functools
+from PIL import Image
+
+import paddle.fluid.profiler as profiler
+import paddle.fluid as fluid
+
+from hapi.model import Input, set_device
+from hapi.datasets.folder import ImageFolder
+from hapi.vision.transforms import BatchCompose
+
+from utility import add_arguments, print_arguments
+from utility import postprocess, index2word
+from seq2seq_attn import Seq2SeqAttInferModel, WeightCrossEntropy
+import data
+
+parser = argparse.ArgumentParser(description=__doc__)
+add_arg = functools.partial(add_arguments, argparser=parser)
+# yapf: disable
+add_arg('batch_size',        int,   1,       "Minibatch size.")
+add_arg('image_path',        str,   None,    "The directory of images to be used for test.")
+add_arg('init_model',        str,   None,    "The init model file of directory.")
+add_arg('use_gpu',           bool,  True,    "Whether use GPU to train.")
+# model hyper paramters
+add_arg('encoder_size',      int,   200,     "Encoder size.")
+add_arg('decoder_size',      int,   128,     "Decoder size.")
+add_arg('embedding_dim',     int,   128,     "Word vector dim.")
+add_arg('num_classes',       int,   95,      "Number classes.")
+add_arg('beam_size',         int,   3,       "Beam size for beam search.")
+add_arg('dynamic',           bool,  False,   "Whether to use dygraph.")
+# yapf: enable
+
+
+def main(FLAGS):
+    device = set_device("gpu" if FLAGS.use_gpu else "cpu")
+    fluid.enable_dygraph(device) if FLAGS.dynamic else None
+    model = Seq2SeqAttInferModel(
+        encoder_size=FLAGS.encoder_size,
+        decoder_size=FLAGS.decoder_size,
+        emb_dim=FLAGS.embedding_dim,
+        num_classes=FLAGS.num_classes,
+        beam_size=FLAGS.beam_size)
+
+    inputs = [Input([None, 1, 48, 384], "float32", name="pixel"), ]
+
+    model.prepare(inputs=inputs, device=device)
+    model.load(FLAGS.init_model)
+
+    fn = lambda p: Image.open(p).convert('L')
+    test_dataset = ImageFolder(FLAGS.image_path, loader=fn)
+    test_collate_fn = BatchCompose([data.Resize(), data.Normalize()])
+    test_loader = fluid.io.DataLoader(
+        test_dataset,
+        places=device,
+        num_workers=0,
+        return_list=True,
+        collate_fn=test_collate_fn)
+
+    samples = test_dataset.samples
+    #outputs = model.predict(test_loader)
+    ins_id = 0
+    for image, in test_loader:
+        image = image if FLAGS.dynamic else image[0]
+        pred = model.test_batch([image])[0]
+        pred = pred[:, :, np.newaxis] if len(pred.shape) == 2 else pred
+        pred = np.transpose(pred, [0, 2, 1])
+        for ins in pred:
+            impath = samples[ins_id]
+            ins_id += 1
+            print('Image {}: {}'.format(ins_id, impath))
+            for beam_idx, beam in enumerate(ins):
+                id_list = postprocess(beam)
+                word_list = index2word(id_list)
+                sequence = "".join(word_list)
+                print('{}: {}'.format(beam_idx, sequence))
+
+
+if __name__ == '__main__':
+    FLAGS = parser.parse_args()
+    print_arguments(FLAGS)
+    main(FLAGS)

examples/ocr/seq2seq_attn.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import print_function
+
+import numpy as np
+
+import paddle.fluid as fluid
+import paddle.fluid.layers as layers
+from paddle.fluid.layers import BeamSearchDecoder
+
+from hapi.text import RNNCell, RNN, DynamicDecode
+from hapi.model import Model, Loss
+
+
+class ConvBNPool(fluid.dygraph.Layer):
+    def __init__(self,
+                 in_ch,
+                 out_ch,
+                 act="relu",
+                 is_test=False,
+                 pool=True,
+                 use_cudnn=True):
+        super(ConvBNPool, self).__init__()
+        self.pool = pool
+
+        filter_size = 3
+        std = (2.0 / (filter_size**2 * in_ch))**0.5
+        param_0 = fluid.ParamAttr(
+            initializer=fluid.initializer.Normal(0.0, std))
+
+        std = (2.0 / (filter_size**2 * out_ch))**0.5
+        param_1 = fluid.ParamAttr(
+            initializer=fluid.initializer.Normal(0.0, std))
+
+        self.conv0 = fluid.dygraph.Conv2D(
+            in_ch,
+            out_ch,
+            3,
+            padding=1,
+            param_attr=param_0,
+            bias_attr=False,
+            act=None,
+            use_cudnn=use_cudnn)
+        self.bn0 = fluid.dygraph.BatchNorm(out_ch, act=act)
+        self.conv1 = fluid.dygraph.Conv2D(
+            out_ch,
+            out_ch,
+            filter_size=3,
+            padding=1,
+            param_attr=param_1,
+            bias_attr=False,
+            act=None,
+            use_cudnn=use_cudnn)
+        self.bn1 = fluid.dygraph.BatchNorm(out_ch, act=act)
+
+        if self.pool:
+            self.pool = fluid.dygraph.Pool2D(
+                pool_size=2,
+                pool_type='max',
+                pool_stride=2,
+                use_cudnn=use_cudnn,
+                ceil_mode=True)
+
+    def forward(self, inputs):
+        out = self.conv0(inputs)
+        out = self.bn0(out)
+        out = self.conv1(out)
+        out = self.bn1(out)
+        if self.pool:
+            out = self.pool(out)
+        return out
+
+
+class CNN(fluid.dygraph.Layer):
+    def __init__(self, in_ch=1, is_test=False):
+        super(CNN, self).__init__()
+        self.conv_bn1 = ConvBNPool(in_ch, 16)
+        self.conv_bn2 = ConvBNPool(16, 32)
+        self.conv_bn3 = ConvBNPool(32, 64)
+        self.conv_bn4 = ConvBNPool(64, 128, pool=False)
+
+    def forward(self, inputs):
+        conv = self.conv_bn1(inputs)
+        conv = self.conv_bn2(conv)
+        conv = self.conv_bn3(conv)
+        conv = self.conv_bn4(conv)
+        return conv
+
+
+class GRUCell(RNNCell):
+    def __init__(self,
+                 input_size,
+                 hidden_size,
+                 param_attr=None,
+                 bias_attr=None,
+                 gate_activation='sigmoid',
+                 candidate_activation='tanh',
+                 origin_mode=False):
+        super(GRUCell, self).__init__()
+        self.hidden_size = hidden_size
+        self.fc_layer = fluid.dygraph.Linear(
+            input_size,
+            hidden_size * 3,
+            param_attr=param_attr,
+            bias_attr=False)
+
+        self.gru_unit = fluid.dygraph.GRUUnit(
+            hidden_size * 3,
+            param_attr=param_attr,
+            bias_attr=bias_attr,
+            activation=candidate_activation,
+            gate_activation=gate_activation,
+            origin_mode=origin_mode)
+
+    def forward(self, inputs, states):
+        # step_outputs, new_states = cell(step_inputs, states)
+        # for GRUCell, `step_outputs` and `new_states` both are hidden
+        x = self.fc_layer(inputs)
+        hidden, _, _ = self.gru_unit(x, states)
+        return hidden, hidden
+
+    @property
+    def state_shape(self):
+        return [self.hidden_size]
+
+
+class Encoder(fluid.dygraph.Layer):
+    def __init__(
+            self,
+            in_channel=1,
+            rnn_hidden_size=200,
+            decoder_size=128,
+            is_test=False, ):
+        super(Encoder, self).__init__()
+        self.rnn_hidden_size = rnn_hidden_size
+
+        self.backbone = CNN(in_ch=in_channel, is_test=is_test)
+
+        para_attr = fluid.ParamAttr(
+            initializer=fluid.initializer.Normal(0.0, 0.02))
+        bias_attr = fluid.ParamAttr(
+            initializer=fluid.initializer.Normal(0.0, 0.02), learning_rate=2.0)
+        self.gru_fwd = RNN(cell=GRUCell(
+            input_size=128 * 6,
+            hidden_size=rnn_hidden_size,
+            param_attr=para_attr,
+            bias_attr=bias_attr,
+            candidate_activation='relu'),
+                           is_reverse=False,
+                           time_major=False)
+        self.gru_bwd = RNN(cell=GRUCell(
+            input_size=128 * 6,
+            hidden_size=rnn_hidden_size,
+            param_attr=para_attr,
+            bias_attr=bias_attr,
+            candidate_activation='relu'),
+                           is_reverse=True,
+                           time_major=False)
+        self.encoded_proj_fc = fluid.dygraph.Linear(
+            rnn_hidden_size * 2, decoder_size, bias_attr=False)
+
+    def forward(self, inputs):
+        conv_features = self.backbone(inputs)
+        conv_features = fluid.layers.transpose(
+            conv_features, perm=[0, 3, 1, 2])
+
+        n, w, c, h = conv_features.shape
+        seq_feature = fluid.layers.reshape(conv_features, [0, -1, c * h])
+
+        gru_fwd, _ = self.gru_fwd(seq_feature)
+        gru_bwd, _ = self.gru_bwd(seq_feature)
+
+        encoded_vector = fluid.layers.concat(input=[gru_fwd, gru_bwd], axis=2)
+        encoded_proj = self.encoded_proj_fc(encoded_vector)
+        return gru_bwd, encoded_vector, encoded_proj
+
+
+class Attention(fluid.dygraph.Layer):
+    """
+    Neural Machine Translation by Jointly Learning to Align and Translate.
+    https://arxiv.org/abs/1409.0473
+    """
+
+    def __init__(self, decoder_size):
+        super(Attention, self).__init__()
+        self.fc1 = fluid.dygraph.Linear(
+            decoder_size, decoder_size, bias_attr=False)
+        self.fc2 = fluid.dygraph.Linear(decoder_size, 1, bias_attr=False)
+
+    def forward(self, encoder_vec, encoder_proj, decoder_state):
+        # alignment model, single-layer multilayer perceptron
+        decoder_state = self.fc1(decoder_state)
+        decoder_state = fluid.layers.unsqueeze(decoder_state, [1])
+
+        e = fluid.layers.elementwise_add(encoder_proj, decoder_state)
+        e = fluid.layers.tanh(e)
+
+        att_scores = self.fc2(e)
+        att_scores = fluid.layers.squeeze(att_scores, [2])
+        att_scores = fluid.layers.softmax(att_scores)
+
+        context = fluid.layers.elementwise_mul(
+            x=encoder_vec, y=att_scores, axis=0)
+        context = fluid.layers.reduce_sum(context, dim=1)
+        return context
+
+
+class DecoderCell(RNNCell):
+    def __init__(self, encoder_size=200, decoder_size=128):
+        super(DecoderCell, self).__init__()
+        self.attention = Attention(decoder_size)
+        self.gru_cell = GRUCell(
+            input_size=encoder_size * 2 + decoder_size,
+            hidden_size=decoder_size)
+
+    def forward(self, current_word, states, encoder_vec, encoder_proj):
+        context = self.attention(encoder_vec, encoder_proj, states)
+        decoder_inputs = fluid.layers.concat([current_word, context], axis=1)
+        hidden, _ = self.gru_cell(decoder_inputs, states)
+        return hidden, hidden
+
+
+class Decoder(fluid.dygraph.Layer):
+    def __init__(self, num_classes, emb_dim, encoder_size, decoder_size):
+        super(Decoder, self).__init__()
+        self.decoder_attention = RNN(DecoderCell(encoder_size, decoder_size))
+        self.fc = fluid.dygraph.Linear(
+            decoder_size, num_classes + 2, act='softmax')
+
+    def forward(self, target, initial_states, encoder_vec, encoder_proj):
+        out, _ = self.decoder_attention(
+            target,
+            initial_states=initial_states,
+            encoder_vec=encoder_vec,
+            encoder_proj=encoder_proj)
+        pred = self.fc(out)
+        return pred
+
+
+class Seq2SeqAttModel(Model):
+    def __init__(
+            self,
+            in_channle=1,
+            encoder_size=200,
+            decoder_size=128,
+            emb_dim=128,
+            num_classes=None, ):
+        super(Seq2SeqAttModel, self).__init__()
+        self.encoder = Encoder(in_channle, encoder_size, decoder_size)
+        self.fc = fluid.dygraph.Linear(
+            input_dim=encoder_size,
+            output_dim=decoder_size,
+            bias_attr=False,
+            act='relu')
+        self.embedding = fluid.dygraph.Embedding(
+            [num_classes + 2, emb_dim], dtype='float32')
+        self.decoder = Decoder(num_classes, emb_dim, encoder_size,
+                               decoder_size)
+
+    def forward(self, inputs, target):
+        gru_backward, encoded_vector, encoded_proj = self.encoder(inputs)
+        decoder_boot = self.fc(gru_backward[:, 0])
+        trg_embedding = self.embedding(target)
+        prediction = self.decoder(trg_embedding, decoder_boot, encoded_vector,
+                                  encoded_proj)
+        return prediction
+
+
+class Seq2SeqAttInferModel(Seq2SeqAttModel):
+    def __init__(
+            self,
+            in_channle=1,
+            encoder_size=200,
+            decoder_size=128,
+            emb_dim=128,
+            num_classes=None,
+            beam_size=0,
+            bos_id=0,
+            eos_id=1,
+            max_out_len=20, ):
+        super(Seq2SeqAttInferModel, self).__init__(
+            in_channle, encoder_size, decoder_size, emb_dim, num_classes)
+        self.beam_size = beam_size
+        # dynamic decoder for inference
+        decoder = BeamSearchDecoder(
+            self.decoder.decoder_attention.cell,
+            start_token=bos_id,
+            end_token=eos_id,
+            beam_size=beam_size,
+            embedding_fn=self.embedding,
+            output_fn=self.decoder.fc)
+        self.infer_decoder = DynamicDecode(
+            decoder, max_step_num=max_out_len, is_test=True)
+
+    def forward(self, inputs, *args):
+        gru_backward, encoded_vector, encoded_proj = self.encoder(inputs)
+        decoder_boot = self.fc(gru_backward[:, 0])
+
+        if self.beam_size:
+            # Tile the batch dimension with beam_size
+            encoded_vector = BeamSearchDecoder.tile_beam_merge_with_batch(
+                encoded_vector, self.beam_size)
+            encoded_proj = BeamSearchDecoder.tile_beam_merge_with_batch(
+                encoded_proj, self.beam_size)
+        # dynamic decoding with beam search
+        rs, _ = self.infer_decoder(
+            inits=decoder_boot,
+            encoder_vec=encoded_vector,
+            encoder_proj=encoded_proj)
+        return rs
+
+
+class WeightCrossEntropy(Loss):
+    def __init__(self):
+        super(WeightCrossEntropy, self).__init__(average=False)
+
+    def forward(self, outputs, labels):
+        predict, (label, mask) = outputs[0], labels
+        loss = layers.cross_entropy(predict, label=label)
+        loss = layers.elementwise_mul(loss, mask, axis=0)
+        loss = layers.reduce_sum(loss)
+        return loss

examples/ocr/train.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import print_function
+
+import os
+import sys
+import random
+import numpy as np
+
+import argparse
+import functools
+
+import paddle.fluid.profiler as profiler
+import paddle.fluid as fluid
+
+from hapi.model import Input, set_device
+from hapi.vision.transforms import BatchCompose
+
+from utility import add_arguments, print_arguments
+from utility import SeqAccuracy, LoggerCallBack
+from seq2seq_attn import Seq2SeqAttModel, WeightCrossEntropy
+import data
+
+parser = argparse.ArgumentParser(description=__doc__)
+add_arg = functools.partial(add_arguments, argparser=parser)
+# yapf: disable
+add_arg('batch_size',        int,   32,           "Minibatch size.")
+add_arg('epoch',             int,   30,           "Epoch number.")
+add_arg('num_workers',       int,   0,            "workers number.")
+add_arg('lr',                float, 0.001,        "Learning rate.")
+add_arg('lr_decay_strategy', str,   "",           "Learning rate decay strategy.")
+add_arg('checkpoint_path',   str,   "checkpoint", "The directory the model to be saved to.")
+add_arg('train_images',      str,   None,         "The directory of images to be used for training.")
+add_arg('train_list',        str,   None,         "The list file of images to be used for training.")
+add_arg('test_images',       str,   None,         "The directory of images to be used for test.")
+add_arg('test_list',         str,   None,         "The list file of images to be used for training.")
+add_arg('resume_path',       str,   None,         "The init model file of directory.")
+add_arg('use_gpu',           bool,  True,         "Whether use GPU to train.")
+# model hyper paramters
+add_arg('encoder_size',      int,   200,     "Encoder size.")
+add_arg('decoder_size',      int,   128,     "Decoder size.")
+add_arg('embedding_dim',     int,   128,     "Word vector dim.")
+add_arg('num_classes',       int,   95,     "Number classes.")
+add_arg('gradient_clip',     float, 5.0,     "Gradient clip value.")
+add_arg('dynamic',           bool,  False,      "Whether to use dygraph.")
+# yapf: enable
+
+
+def main(FLAGS):
+    device = set_device("gpu" if FLAGS.use_gpu else "cpu")
+    fluid.enable_dygraph(device) if FLAGS.dynamic else None
+
+    model = Seq2SeqAttModel(
+        encoder_size=FLAGS.encoder_size,
+        decoder_size=FLAGS.decoder_size,
+        emb_dim=FLAGS.embedding_dim,
+        num_classes=FLAGS.num_classes)
+
+    lr = FLAGS.lr
+    if FLAGS.lr_decay_strategy == "piecewise_decay":
+        learning_rate = fluid.layers.piecewise_decay(
+            [200000, 250000], [lr, lr * 0.1, lr * 0.01])
+    else:
+        learning_rate = lr
+    grad_clip = fluid.clip.GradientClipByGlobalNorm(FLAGS.gradient_clip)
+    optimizer = fluid.optimizer.Adam(
+        learning_rate=learning_rate,
+        parameter_list=model.parameters(),
+        grad_clip=grad_clip)
+
+    # yapf: disable
+    inputs = [
+        Input([None,1,48,384], "float32", name="pixel"),
+        Input([None, None], "int64", name="label_in"),
+    ]
+    labels = [
+        Input([None, None], "int64", name="label_out"),
+        Input([None, None], "float32", name="mask"),
+    ]
+    # yapf: enable
+
+    model.prepare(
+        optimizer,
+        WeightCrossEntropy(),
+        SeqAccuracy(),
+        inputs=inputs,
+        labels=labels)
+
+    train_dataset = data.train()
+    train_collate_fn = BatchCompose(
+        [data.Resize(), data.Normalize(), data.PadTarget()])
+    train_sampler = data.BatchSampler(
+        train_dataset, batch_size=FLAGS.batch_size, shuffle=True)
+    train_loader = fluid.io.DataLoader(
+        train_dataset,
+        batch_sampler=train_sampler,
+        places=device,
+        num_workers=FLAGS.num_workers,
+        return_list=True,
+        collate_fn=train_collate_fn)
+    test_dataset = data.test()
+    test_collate_fn = BatchCompose(
+        [data.Resize(), data.Normalize(), data.PadTarget()])
+    test_sampler = data.BatchSampler(
+        test_dataset,
+        batch_size=FLAGS.batch_size,
+        drop_last=False,
+        shuffle=False)
+    test_loader = fluid.io.DataLoader(
+        test_dataset,
+        batch_sampler=test_sampler,
+        places=device,
+        num_workers=0,
+        return_list=True,
+        collate_fn=test_collate_fn)
+
+    model.fit(train_data=train_loader,
+              eval_data=test_loader,
+              epochs=FLAGS.epoch,
+              save_dir=FLAGS.checkpoint_path,
+              callbacks=[LoggerCallBack(10, 2, FLAGS.batch_size)])
+
+
+if __name__ == '__main__':
+    FLAGS = parser.parse_args()
+    print_arguments(FLAGS)
+    main(FLAGS)

examples/ocr/utility.py

+"""Contains common utility functions."""
+#  Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import distutils.util
+import numpy as np
+import paddle.fluid as fluid
+import six
+
+from hapi.metrics import Metric
+from hapi.callbacks import ProgBarLogger
+
+
+def print_arguments(args):
+    """Print argparse's arguments.
+
+    Usage:
+
+    .. code-block:: python
+
+        parser = argparse.ArgumentParser()
+        parser.add_argument("name", default="Jonh", type=str, help="User name.")
+        args = parser.parse_args()
+        print_arguments(args)
+
+    :param args: Input argparse.Namespace for printing.
+    :type args: argparse.Namespace
+    """
+    print("-----------  Configuration Arguments -----------")
+    for arg, value in sorted(six.iteritems(vars(args))):
+        print("%s: %s" % (arg, value))
+    print("------------------------------------------------")
+
+
+def add_arguments(argname, type, default, help, argparser, **kwargs):
+    """Add argparse's argument.
+
+    Usage:
+
+    .. code-block:: python
+
+        parser = argparse.ArgumentParser()
+        add_argument("name", str, "Jonh", "User name.", parser)
+        args = parser.parse_args()
+    """
+    type = distutils.util.strtobool if type == bool else type
+    argparser.add_argument(
+        "--" + argname,
+        default=default,
+        type=type,
+        help=help + ' Default: %(default)s.',
+        **kwargs)
+
+
+class SeqAccuracy(Metric):
+    def __init__(self, name=None, *args, **kwargs):
+        super(SeqAccuracy, self).__init__(*args, **kwargs)
+        self._name = 'seq_acc'
+        self.reset()
+
+    def add_metric_op(self, output, label, mask, *args, **kwargs):
+        pred = fluid.layers.flatten(output, axis=2)
+        score, topk = fluid.layers.topk(pred, 1)
+        return topk, label, mask
+
+    def update(self, topk, label, mask, *args, **kwargs):
+        topk = topk.reshape(label.shape[0], -1)
+        seq_len = np.sum(mask, -1)
+        acc = 0
+        for i in range(label.shape[0]):
+            l = int(seq_len[i] - 1)
+            pred = topk[i][:l - 1]
+            ref = label[i][:l - 1]
+            if np.array_equal(pred, ref):
+                self.total += 1
+                acc += 1
+            self.count += 1
+        return float(acc) / label.shape[0]
+
+    def reset(self):
+        self.total = 0.
+        self.count = 0.
+
+    def accumulate(self):
+        return float(self.total) / self.count
+
+    def name(self):
+        return self._name
+
+
+class LoggerCallBack(ProgBarLogger):
+    def __init__(self, log_freq=1, verbose=2, train_bs=None, eval_bs=None):
+        super(LoggerCallBack, self).__init__(log_freq, verbose)
+        self.train_bs = train_bs
+        self.eval_bs = eval_bs if eval_bs else train_bs
+
+    def on_train_batch_end(self, step, logs=None):
+        logs = logs or {}
+        logs['loss'] = [l / self.train_bs for l in logs['loss']]
+        super(LoggerCallBack, self).on_train_batch_end(step, logs)
+
+    def on_epoch_end(self, epoch, logs=None):
+        logs = logs or {}
+        logs['loss'] = [l / self.train_bs for l in logs['loss']]
+        super(LoggerCallBack, self).on_epoch_end(epoch, logs)
+
+    def on_eval_batch_end(self, step, logs=None):
+        logs = logs or {}
+        logs['loss'] = [l / self.eval_bs for l in logs['loss']]
+        super(LoggerCallBack, self).on_eval_batch_end(step, logs)
+
+    def on_eval_end(self, logs=None):
+        logs = logs or {}
+        logs['loss'] = [l / self.eval_bs for l in logs['loss']]
+        super(LoggerCallBack, self).on_eval_end(logs)
+
+
+def index2word(ids):
+    return [chr(int(k + 33)) for k in ids]
+
+
+def postprocess(seq, bos_idx=0, eos_idx=1):
+    if type(seq) is np.ndarray:
+        seq = seq.tolist()
+    eos_pos = len(seq) - 1
+    for i, idx in enumerate(seq):
+        if idx == eos_idx:
+            eos_pos = i
+            break
+    seq = [
+        idx for idx in seq[:eos_pos + 1] if idx != bos_idx and idx != eos_idx
+    ]
+    return seq
+
+
+class SeqBeamAccuracy(Metric):
+    def __init__(self, name=None, *args, **kwargs):
+        super(SeqBeamAccuracy, self).__init__(*args, **kwargs)
+        self._name = 'seq_acc'
+        self.reset()
+
+    def add_metric_op(self, output, label, mask, *args, **kwargs):
+        return output, label, mask
+
+    def update(self, preds, labels, masks, *args, **kwargs):
+        preds = preds[:, :, np.newaxis] if len(preds.shape) == 2 else preds
+        preds = np.transpose(preds, [0, 2, 1])
+        seq_len = np.sum(masks, -1)
+        acc = 0
+        for i in range(labels.shape[0]):
+            l = int(seq_len[i] - 1)
+            #ref = labels[i][: l - 1]
+            ref = np.array(postprocess(labels[i]))
+            pred = preds[i]
+            for idx, beam in enumerate(pred):
+                beam_pred = np.array(postprocess(beam))
+                if np.array_equal(beam_pred, ref):
+                    self.total += 1
+                    acc += 1
+                    break
+            self.count += 1
+        return float(acc) / labels.shape[0]
+
+    def reset(self):
+        self.total = 0.
+        self.count = 0.
+
+    def accumulate(self):
+        return float(self.total) / self.count
+
+    def name(self):
+        return self._name

examples/sequence_tagging/README.md

+# 序列标注任务
+
+## 1. 简介
+
+Sequence Tagging，是一个序列标注模型，模型可用于实现，分词、词性标注、专名识别等序列标注任务。我们在自建的数据集上对分词、词性标注、专名识别进行整体的评估效果（即联合标签模型），具体数值见下表；
+
+|模型|Precision|Recall|F1-score|
+|:-:|:-:|:-:|:-:|
+|Lexical Analysis|88.26%|89.20%|88.73%|
+
+## 2. 快速开始
+
+### 安装说明
+
+#### 1.PaddlePaddle 安装
+
+本项目依赖 PaddlePaddle 1.7 及以上版本和PaddleHub 1.0.0及以上版本 ，PaddlePaddle安装请参考官网 [快速安装](http://www.paddlepaddle.org/paddle#quick-start)，PaddleHub安装参考 [PaddleHub](https://github.com/PaddlePaddle/PaddleHub)。
+
+> Warning: GPU 和 CPU 版本的 PaddlePaddle 分别是 paddlepaddle-gpu 和 paddlepaddle，请安装时注意区别。
+
+#### 2. 克隆代码
+克隆工具集代码库到本地
+```bash
+ git clone https://github.com/PaddlePaddle/hapi.git
+ cd hapi/sequence_tagging
+```
+
+#### 3. 环境依赖
+PaddlePaddle的版本要求是：Python 2 版本是 2.7.15+、Python 3 版本是 3.5.1+/3.6/3.7。sequence tagging的代码可支持Python2/3，无具体版本限制
+
+### 数据准备
+
+#### 1. 快速下载
+
+本项目涉及的**数据集**和**训练模型**的数据可通过执行以下脚本进行快速下载，若仅需使用部分数据或者模型，可根据需要参照2和3进行下载
+
+```bash
+python downloads.py all
+```
+或在支持运行shell脚本的环境下执行：
+```bash
+sh downloads.sh
+```
+
+#### 2. 训练数据集
+
+下载数据集文件，解压后会生成 `./data/` 文件夹
+```bash
+python downloads.py dataset
+```
+
+#### 3. 已训练模型
+
+我们开源了在自建数据集上训练的词法分析模型，可供用户直接使用，可通过下述链接进行下载:
+```bash
+# download baseline model
+python downloads.py model
+```
+
+### 模型训练
+基于示例的数据集，可通过下面的命令，在训练集 `./data/train.tsv` 上进行训练；
+
+GPU上单卡训练
+```
+# setting visible devices for training
+export CUDA_VISIBLE_DEVICES=0
+
+python -u train.py \
+          --device gpu \
+          --dynamic False
+
+# --device: 使用gpu设备还是cpu设备
+# --dynamic： 是否使用动态图模式进行训练，如果使用静态图训练，设置为True, 动态图设置为False
+```
+
+GPU上多卡训练
+
+```
+# setting visible devices for training
+export CUDA_VISIBLE_DEVICES=0,1,2,3
+
+python -m paddle.distributed.launch --selected_gpus=0,1,2,3  train.py \
+          --device gpu \
+          --dynamic False
+
+# --device: 使用gpu设备还是cpu设备
+# --dynamic: 是否使用动态图模式进行训练，如果使用静态图训练，设置为True, 动态图设置为False
+```
+
+CPU上训练
+
+```
+python -u train.py \
+          --device cpu \
+          --dynamic False
+
+# --device: 使用gpu设备还是cpu设备
+# --dynamic: 是否使用动态图模式进行训练，如果使用静态图训练，设置为True, 动态图设置为False
+```
+
+### 模型预测
+
+加载已有的模型，对未知的数据进行预测
+```bash
+python predict.py \
+      --init_from_checkpoint  model_baseline/params \
+      --output_file predict.result  \
+      --mode predict \
+      --device cpu  \
+      --dynamic False
+
+# --init_from_checkpoint: 初始化模型
+# --output_file: 预测结果文件
+# --device: 使用gpu还是cpu设备
+# --mode: 开启模式, 设置为train时，进行训练，设置为predict时进行预测
+# --dynamic: 是否使用动态图模式进行训练，如果使用静态图训练，设置为True, 动态图设置为False
+```
+
+### 模型评估
+
+我们基于自建的数据集训练了一个词法分析的模型，可以直接用这个模型对测试集 `./data/test.tsv` 进行验证，
+```bash
+# baseline model
+python eval.py \
+        --init_from_checkpoint  ./model_baseline/params \
+        --mode predict \
+        --device cpu  \
+        --dynamic False
+
+# --init_from_checkpoint: 初始化模型
+# --device: 使用gpu还是cpu设备
+# --mode: 开启模式, 设置为train时，进行训练，设置为predict时进行预测
+# --dynamic: 是否使用动态图模式进行训练，如果使用静态图训练，设置为True, 动态图设置为False
+```
+
+
+## 3. 进阶使用
+
+### 任务定义与建模
+序列标注任务的输入是一个字符串（我们后面使用『句子』来指代它），而输出是句子中的词边界和类别。序列标注是词法分析的经典建模方式。我们使用基于 GRU 的网络结构学习特征，将学习到的特征接入 CRF 解码层完成序列标注。CRF 解码层本质上是将传统 CRF 中的线性模型换成了非线性神经网络，基于句子级别的似然概率，因而能够更好的解决标记偏置问题。模型要点如下。
+
+1. 输入采用 one-hot 方式表示，每个字以一个 id 表示
+2. one-hot 序列通过字表，转换为实向量表示的字向量序列；
+3. 字向量序列作为双向 GRU 的输入，学习输入序列的特征表示，得到新的特性表示序列，我们堆叠了两层双向GRU以增加学习能力；
+4. CRF 以 GRU 学习到的特征为输入，以标记序列为监督信号，实现序列标注。
+
+可供用户下载的自有数据是对分词、词性标注、专名识别同时标注的联合数据集，进行词性和专名类别标签集合如下表，其中词性标签 24 个（小写字母），专名类别标签 4 个（大写字母）。这里需要说明的是，人名、地名、机构名和时间四个类别，在上表中存在两套标签（PER / LOC / ORG / TIME 和 nr / ns / nt / t），被标注为第二套标签的词，是模型判断为低置信度的人名、地名、机构名和时间词。开发者可以基于这两套标签，在四个类别的准确、召回之间做出自己的权衡。
+
+| 标签 | 含义     | 标签 | 含义     | 标签 | 含义     | 标签 | 含义     |
+| ---- | -------- | ---- | -------- | ---- | -------- | ---- | -------- |
+| n    | 普通名词 | f    | 方位名词 | s    | 处所名词 | t    | 时间     |
+| nr   | 人名     | ns   | 地名     | nt   | 机构名   | nw   | 作品名   |
+| nz   | 其他专名 | v    | 普通动词 | vd   | 动副词   | vn   | 名动词   |
+| a    | 形容词   | ad   | 副形词   | an   | 名形词   | d    | 副词     |
+| m    | 数量词   | q    | 量词     | r    | 代词     | p    | 介词     |
+| c    | 连词     | u    | 助词     | xc   | 其他虚词 | w    | 标点符号 |
+| PER  | 人名     | LOC  | 地名     | ORG  | 机构名   | TIME | 时间     |
+
+### 模型原理介绍
+上面介绍的模型原理如下图所示：<br />
+
+<p align="center">
+<img src="./images/gru-crf-model.png" width = "340" height = "300" /> <br />
+Overall Architecture of GRU-CRF-MODEL
+</p>
+
+### 数据格式
+训练使用的数据可以由用户根据实际的应用场景，自己组织数据。除了第一行是 `text_a\tlabel` 固定的开头，后面的每行数据都是由两列组成，以制表符分隔，第一列是 utf-8 编码的中文文本，以 `\002` 分割，第二列是对应每个字的标注，以 `\002` 分隔。我们采用 IOB2 标注体系，即以 X-B 作为类型为 X 的词的开始，以 X-I 作为类型为 X 的词的持续，以 O 表示不关注的字（实际上，在词性、专名联合标注中，不存在 O ）。示例如下：
+
+```text
+除\002了\002他\002续\002任\002十\002二\002届\002政\002协\002委\002员\002,\002马\002化\002腾\002,\002雷\002军\002,\002李\002彦\002宏\002也\002被\002推\002选\002为\002新\002一\002届\002全\002国\002人\002大\002代\002表\002或\002全\002国\002政\002协\002委\002员	p-B\002p-I\002r-B\002v-B\002v-I\002m-B\002m-I\002m-I\002ORG-B\002ORG-I\002n-B\002n-I\002w-B\002PER-B\002PER-I\002PER-I\002w-B\002PER-B\002PER-I\002w-B\002PER-B\002PER-I\002PER-I\002d-B\002p-B\002v-B\002v-I\002v-B\002a-B\002m-B\002m-I\002ORG-B\002ORG-I\002ORG-I\002ORG-I\002n-B\002n-I\002c-B\002n-B\002n-I\002ORG-B\002ORG-I\002n-B\002n-I
+```
+
++ 我们随同代码一并发布了完全版的模型和相关的依赖数据。但是，由于模型的训练数据过于庞大，我们没有发布训练数据，仅在`data`目录下放置少数样本用以示例输入数据格式。
+
++ 模型依赖数据包括：
+    1. 输入文本的词典，在`conf`目录下，对应`word.dic`
+    2. 对输入文本中特殊字符进行转换的字典，在`conf`目录下，对应`q2b.dic`
+    3. 标记标签的词典,在`conf`目录下，对应`tag.dic`
+
++ 在训练和预测阶段，我们都需要进行原始数据的预处理，具体处理工作包括：
+
+    1. 从原始数据文件中抽取出句子和标签，构造句子序列和标签序列
+    2. 将句子序列中的特殊字符进行转换
+    3. 依据词典获取词对应的整数索引
+
+### 代码结构说明
+```text
+├── README.md                          # 本文档
+├── data/                                   # 存放数据集的目录
+├── conf/                                   # 词典及程序默认配置的目录
+├── images/                               # 文档图片存放位置
+├── utils/                                   # 常用工具函数
+├── train.py                               # 训练脚本
+├── predict.py                           # 预测脚本
+├── eval.py                               # 词法分析评估的脚本
+├── downloads.py                      # 用于下载数据和模型的脚本
+├── downloads.sh                      # 用于下载数据和模型的脚本
+└──reader.py                           # 文件读取相关函数
+```
+
+
+## 4. 其他
+### 在论文中引用 sequence tagging
+
+如果您的学术工作成果中使用了 sequence tagging，请您增加下述引用。我们非常欣慰sequence tagging模型能够对您的学术工作带来帮助。
+
+```text
+@article{jiao2018LAC,
+	title={Chinese Lexical Analysis with Deep Bi-GRU-CRF Network},
+	author={Jiao, Zhenyu and Sun, Shuqi and Sun, Ke},
+	journal={arXiv preprint arXiv:1807.01882},
+	year={2018},
+	url={https://arxiv.org/abs/1807.01882}
+}
+```
+### 如何贡献代码
+如果你可以修复某个 issue 或者增加一个新功能，欢迎给我们提交PR。如果对应的PR被接受了，我们将根据贡献的质量和难度 进行打分（0-5分，越高越好）。如果你累计获得了 10 分，可以联系我们获得面试机会或为你写推荐信。

examples/sequence_tagging/conf/q2b.dic

+　	 
+、	,
+。	.
+—	-
+～	~
+‖	|
+…	.
+‘	'
+’	'
+“	"
+”	"
+〔	(
+〕	)
+〈	<
+〉	>
+「	'
+」	'
+『	"
+』	"
+〖	[
+〗	]
+【	[
+】	]
+∶	:
+＄	$
+！	!
+＂	"
+＃	#
+％	%
+＆	&
+＇	'
+（	(
+）	)
+＊	*
+＋	+
+，	,
+－	-
+．	.
+／	/
+０	0
+１	1
+２	2
+３	3
+４	4
+５	5
+６	6
+７	7
+８	8
+９	9
+：	:
+；	;
+＜	<
+＝	=
+＞	>
+？	?
+＠	@
+Ａ	a
+Ｂ	b
+Ｃ	c
+Ｄ	d
+Ｅ	e
+Ｆ	f
+Ｇ	g
+Ｈ	h
+Ｉ	i
+Ｊ	j
+Ｋ	k
+Ｌ	l
+Ｍ	m
+Ｎ	n
+Ｏ	o
+Ｐ	p
+Ｑ	q
+Ｒ	r
+Ｓ	s
+Ｔ	t
+Ｕ	u
+Ｖ	v
+Ｗ	w
+Ｘ	x
+Ｙ	y
+Ｚ	z
+［	[
+＼	\
+］	]
+＾	^
+＿	_
+｀	`
+ａ	a
+ｂ	b
+ｃ	c
+ｄ	d
+ｅ	e
+ｆ	f
+ｇ	g
+ｈ	h
+ｉ	i
+ｊ	j
+ｋ	k
+ｌ	l
+ｍ	m
+ｎ	n
+ｏ	o
+ｐ	p
+ｑ	q
+ｒ	r
+ｓ	s
+ｔ	t
+ｕ	u
+ｖ	v
+ｗ	w
+ｘ	x
+ｙ	y
+ｚ	z
+｛	{
+｜	|
+｝	}
+￣	~
+〝	"
+〞	"
+﹐	,
+﹑	,
+﹒	.
+﹔	;
+﹕	:
+﹖	?
+﹗	!
+﹙	(
+﹚	)
+﹛	{
+﹜	{
+﹝	[
+﹞	]
+﹟	#
+﹠	&
+﹡	*
+﹢	+
+﹣	-
+﹤	<
+﹥	>
+﹦	=
+﹨	\
+﹩	$
+﹪	%
+﹫	@
+ 	,
+A	a
+B	b
+C	c
+D	d
+E	e
+F	f
+G	g
+H	h
+I	i
+J	j
+K	k
+L	l
+M	m
+N	n
+O	o
+P	p
+Q	q
+R	r
+S	s
+T	t
+U	u
+V	v
+W	w
+X	x
+Y	y
+Z	z

examples/sequence_tagging/conf/tag.dic

+0	a-B
+1	a-I
+2	ad-B
+3	ad-I
+4	an-B
+5	an-I
+6	c-B
+7	c-I
+8	d-B
+9	d-I
+10	f-B
+11	f-I
+12	m-B
+13	m-I
+14	n-B
+15	n-I
+16	nr-B
+17	nr-I
+18	ns-B
+19	ns-I
+20	nt-B
+21	nt-I
+22	nw-B
+23	nw-I
+24	nz-B
+25	nz-I
+26	p-B
+27	p-I
+28	q-B
+29	q-I
+30	r-B
+31	r-I
+32	s-B
+33	s-I
+34	t-B
+35	t-I
+36	u-B
+37	u-I
+38	v-B
+39	v-I
+40	vd-B
+41	vd-I
+42	vn-B
+43	vn-I
+44	w-B
+45	w-I
+46	xc-B
+47	xc-I
+48	PER-B
+49	PER-I
+50	LOC-B
+51	LOC-I
+52	ORG-B
+53	ORG-I
+54	TIME-B
+55	TIME-I
+56	O

examples/sequence_tagging/downloads.py

+# 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.
+"""
+Download script, download dataset and pretrain models.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import io
+import os
+import sys
+import time
+import hashlib
+import tarfile
+import requests
+
+FILE_INFO = {
+    'BASE_URL': 'https://baidu-nlp.bj.bcebos.com/',
+    'DATA': {
+        'name': 'lexical_analysis-dataset-2.0.0.tar.gz',
+        'md5': '71e4a9a36d0f0177929a1bccedca7dba'
+    },
+    'MODEL': {
+        'name': 'sequence_tagging_dy.tar.gz',
+        'md5': "1125d374c03c8218b6e47325dcf607e3"
+    },
+}
+
+
+def usage():
+    desc = ("\nDownload datasets and pretrained models for sequence tagging.\n"
+            "Usage:\n"
+            "   1. python download.py all\n"
+            "   2. python download.py dataset\n"
+            "   3. python download.py model\n")
+    print(desc)
+
+
+def md5file(fname):
+    hash_md5 = hashlib.md5()
+    with io.open(fname, "rb") as fin:
+        for chunk in iter(lambda: fin.read(4096), b""):
+            hash_md5.update(chunk)
+    return hash_md5.hexdigest()
+
+
+def extract(fname, dir_path):
+    """
+    Extract tar.gz file
+    """
+    try:
+        tar = tarfile.open(fname, "r:gz")
+        file_names = tar.getnames()
+        for file_name in file_names:
+            tar.extract(file_name, dir_path)
+            print(file_name)
+        tar.close()
+    except Exception as e:
+        raise e
+
+
+def _download(url, filename, md5sum):
+    """
+    Download file and check md5
+    """
+    retry = 0
+    retry_limit = 3
+    chunk_size = 4096
+    while not (os.path.exists(filename) and md5file(filename) == md5sum):
+        if retry < retry_limit:
+            retry += 1
+        else:
+            raise RuntimeError(
+                "Cannot download dataset ({0}) with retry {1} times.".format(
+                    url, retry_limit))
+        try:
+            start = time.time()
+            size = 0
+            res = requests.get(url, stream=True)
+            filesize = int(res.headers['content-length'])
+            if res.status_code == 200:
+                print("[Filesize]: %0.2f MB" % (filesize / 1024 / 1024))
+                # save by chunk
+                with io.open(filename, "wb") as fout:
+                    for chunk in res.iter_content(chunk_size=chunk_size):
+                        if chunk:
+                            fout.write(chunk)
+                            size += len(chunk)
+                            pr = '>' * int(size * 50 / filesize)
+                            print(
+                                '\r[Process ]: %s%.2f%%' %
+                                (pr, float(size / filesize * 100)),
+                                end='')
+            end = time.time()
+            print("\n[CostTime]: %.2f s" % (end - start))
+        except Exception as e:
+            print(e)
+
+
+def download(name, dir_path):
+    url = FILE_INFO['BASE_URL'] + FILE_INFO[name]['name']
+    file_path = os.path.join(dir_path, FILE_INFO[name]['name'])
+
+    if not os.path.exists(dir_path):
+        os.makedirs(dir_path)
+
+    # download data
+    print("Downloading : %s" % name)
+    _download(url, file_path, FILE_INFO[name]['md5'])
+
+    # extract data
+    print("Extracting : %s" % file_path)
+    extract(file_path, dir_path)
+    os.remove(file_path)
+
+
+if __name__ == '__main__':
+    if len(sys.argv) != 2:
+        usage()
+        sys.exit(1)
+    pwd = os.path.join(os.path.dirname(__file__), './')
+    ernie_dir = os.path.join(os.path.dirname(__file__), './pretrained')
+
+    if sys.argv[1] == 'all':
+        download('DATA', pwd)
+        download('MODEL', pwd)
+
+    if sys.argv[1] == "dataset":
+        download('DATA', pwd)
+
+    elif sys.argv[1] == "model":
+        download('MODEL', pwd)
+
+    else:
+        usage()

examples/sequence_tagging/downloads.sh

+#!/bin/bash
+
+# download baseline model file to ./model_baseline/
+if [ -d ./model_baseline/ ]
+then
+    echo "./model_baseline/ directory already existed, ignore download"
+else
+    wget --no-check-certificate https://baidu-nlp.bj.bcebos.com/sequence_tagging_dy.tar.gz
+    tar xvf sequence_tagging_dy.tar.gz
+    /bin/rm sequence_tagging_dy.tar.gz
+fi
+
+# download dataset file to ./data/
+if [ -d ./data/ ]
+then
+    echo "./data/ directory already existed, ignore download"
+else
+    wget --no-check-certificate https://baidu-nlp.bj.bcebos.com/lexical_analysis-dataset-2.0.0.tar.gz
+    tar xvf lexical_analysis-dataset-2.0.0.tar.gz
+    /bin/rm lexical_analysis-dataset-2.0.0.tar.gz
+fi
+

examples/sequence_tagging/eval.py

+#   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.
+"""
+SequenceTagging network structure
+"""
+
+from __future__ import division
+from __future__ import print_function
+
+import io
+import os
+import sys
+import math
+import argparse
+import numpy as np
+
+from train import SeqTagging
+from utils.configure import PDConfig
+from utils.check import check_gpu, check_version
+from utils.metrics import chunk_count
+from reader import LacDataset, create_lexnet_data_generator, create_dataloader
+
+work_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+sys.path.append(os.path.join(work_dir, "../"))
+from hapi.model import set_device, Input
+
+import paddle.fluid as fluid
+from paddle.fluid.optimizer import AdamOptimizer
+from paddle.fluid.layers.utils import flatten
+
+
+def main(args):
+    place = set_device(args.device)
+    fluid.enable_dygraph(place) if args.dynamic else None
+
+    inputs = [Input([None, None], 'int64', name='words'), 
+              Input([None], 'int64', name='length')] 
+
+    feed_list = None if args.dynamic else [x.forward() for x in inputs]
+    dataset = LacDataset(args)
+    eval_path = args.test_file
+
+    chunk_evaluator = fluid.metrics.ChunkEvaluator()
+    chunk_evaluator.reset()
+
+    eval_generator = create_lexnet_data_generator(
+        args, reader=dataset, file_name=eval_path, place=place, mode="test")
+
+    eval_dataset = create_dataloader(
+        eval_generator, place, feed_list=feed_list)
+
+    vocab_size = dataset.vocab_size
+    num_labels = dataset.num_labels
+    model = SeqTagging(args, vocab_size, num_labels)
+
+    optim = AdamOptimizer(
+        learning_rate=args.base_learning_rate,
+        parameter_list=model.parameters())
+
+    model.mode = "test"
+    model.prepare(inputs=inputs)
+    model.load(args.init_from_checkpoint, skip_mismatch=True)
+
+    for data in eval_dataset():
+        if len(data) == 1: 
+            batch_data = data[0]
+            targets = np.array(batch_data[2])
+        else: 
+            batch_data = data
+            targets = batch_data[2].numpy()
+        inputs_data = [batch_data[0], batch_data[1]]
+        crf_decode, length = model.test(inputs=inputs_data)
+        num_infer_chunks, num_label_chunks, num_correct_chunks = chunk_count(crf_decode, targets, length, dataset.id2label_dict)
+        chunk_evaluator.update(num_infer_chunks, num_label_chunks, num_correct_chunks)
+    
+    precision, recall, f1 = chunk_evaluator.eval()
+    print("[test] P: %.5f, R: %.5f, F1: %.5f" % (precision, recall, f1))
+
+
+if __name__ == '__main__': 
+    args = PDConfig(yaml_file="sequence_tagging.yaml")
+    args.build()
+    args.Print()
+
+    use_gpu = True if args.device == "gpu" else False
+    check_gpu(use_gpu)
+    check_version()
+    main(args)

examples/sequence_tagging/predict.py

+#   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.
+"""
+SequenceTagging network structure
+"""
+
+from __future__ import division
+from __future__ import print_function
+
+import io
+import os
+import sys
+import math
+import argparse
+import numpy as np
+
+from train import SeqTagging
+from utils.check import check_gpu, check_version
+from utils.configure import PDConfig
+from reader import LacDataset, create_lexnet_data_generator, create_dataloader
+
+work_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+sys.path.append(os.path.join(work_dir, "../"))
+from hapi.model import set_device, Input
+
+import paddle.fluid as fluid
+from paddle.fluid.optimizer import AdamOptimizer
+from paddle.fluid.layers.utils import flatten
+
+
+def main(args):
+    place = set_device(args.device)
+    fluid.enable_dygraph(place) if args.dynamic else None
+
+    inputs = [Input([None, None], 'int64', name='words'), 
+              Input([None], 'int64', name='length')]
+
+    feed_list = None if args.dynamic else [x.forward() for x in inputs]
+    dataset = LacDataset(args)
+    predict_path = args.predict_file
+
+    predict_generator = create_lexnet_data_generator(
+        args, reader=dataset, file_name=predict_path, place=place, mode="predict")
+
+    predict_dataset = create_dataloader(
+        predict_generator, place, feed_list=feed_list)
+
+    vocab_size = dataset.vocab_size
+    num_labels = dataset.num_labels
+    model = SeqTagging(args, vocab_size, num_labels)
+
+    optim = AdamOptimizer(
+        learning_rate=args.base_learning_rate,
+        parameter_list=model.parameters())
+
+    model.mode = "test"
+    model.prepare(inputs=inputs)
+
+    model.load(args.init_from_checkpoint, skip_mismatch=True)
+
+    f = open(args.output_file, "wb")
+    for data in predict_dataset(): 
+        if len(data) == 1: 
+            input_data = data[0]
+        else: 
+            input_data = data
+        results, length = model.test(inputs=flatten(input_data))
+        for i in range(len(results)): 
+            word_len = length[i]
+            word_ids = results[i][: word_len]
+            tags = [dataset.id2label_dict[str(id)] for id in word_ids]
+            f.write("\002".join(tags) + "\n")
+        
+
+if __name__ == '__main__': 
+    args = PDConfig(yaml_file="sequence_tagging.yaml")
+    args.build()
+    args.Print()
+
+    use_gpu = True if args.device == "gpu" else False
+    check_gpu(use_gpu)
+    check_version()
+    main(args)

examples/sequence_tagging/reader.py

+#   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.
+"""
+SequenceTagging dataset
+"""
+
+from __future__ import division
+from __future__ import print_function
+
+import io
+import numpy as np
+
+import paddle
+
+
+class LacDataset(object):
+    """
+    Load lexical analysis dataset
+    """
+
+    def __init__(self, args):
+        self.word_dict_path = args.word_dict_path
+        self.label_dict_path = args.label_dict_path
+        self.word_rep_dict_path = args.word_rep_dict_path
+        self._load_dict()
+
+    def _load_dict(self):
+        self.word2id_dict = self.load_kv_dict(
+            self.word_dict_path, reverse=True, value_func=np.int64)
+        self.id2word_dict = self.load_kv_dict(self.word_dict_path)
+        self.label2id_dict = self.load_kv_dict(
+            self.label_dict_path, reverse=True, value_func=np.int64)
+        self.id2label_dict = self.load_kv_dict(self.label_dict_path)
+        if self.word_rep_dict_path is None:
+            self.word_replace_dict = dict()
+        else:
+            self.word_replace_dict = self.load_kv_dict(self.word_rep_dict_path)
+
+    def load_kv_dict(self,
+                     dict_path,
+                     reverse=False,
+                     delimiter="\t",
+                     key_func=None,
+                     value_func=None):
+        """
+        Load key-value dict from file
+        """
+        result_dict = {}
+        for line in io.open(dict_path, "r", encoding='utf8'):
+            terms = line.strip("\n").split(delimiter)
+            if len(terms) != 2:
+                continue
+            if reverse:
+                value, key = terms
+            else:
+                key, value = terms
+            if key in result_dict:
+                raise KeyError("key duplicated with [%s]" % (key))
+            if key_func:
+                key = key_func(key)
+            if value_func:
+                value = value_func(value)
+            result_dict[key] = value
+        return result_dict
+
+    @property
+    def vocab_size(self):
+        return max(self.word2id_dict.values()) + 1
+
+    @property
+    def num_labels(self):
+        return max(self.label2id_dict.values()) + 1
+
+    def get_num_examples(self, filename):
+        """num of line of file"""
+        return sum(1 for line in io.open(filename, "r", encoding='utf8'))
+
+    def word_to_ids(self, words):
+        """convert word to word index"""
+        word_ids = []
+        for word in words:
+            word = self.word_replace_dict.get(word, word)
+            if word not in self.word2id_dict:
+                word = "OOV"
+            word_id = self.word2id_dict[word]
+            word_ids.append(word_id)
+
+        return word_ids
+
+    def label_to_ids(self, labels):
+        """convert label to label index"""
+        label_ids = []
+        for label in labels:
+            if label not in self.label2id_dict:
+                label = "O"
+            label_id = self.label2id_dict[label]
+            label_ids.append(label_id)
+        return label_ids
+
+    def file_reader(self,
+                    filename,
+                    mode="train",
+                    batch_size=32,
+                    max_seq_len=126):
+        """
+        yield (word_idx, target_idx) one by one from file,
+            or yield (word_idx, ) in `infer` mode
+        """
+
+        def wrapper():
+            fread = io.open(filename, "r", encoding="utf-8")
+            if mode == "train": 
+                headline = next(fread)
+                headline = headline.strip().split('\t')
+                assert len(headline) == 2 and headline[0] == "text_a" and headline[
+                    1] == "label"
+                buf = []
+                for line in fread:
+                    words, labels = line.strip("\n").split("\t")
+                    if len(words) < 1:
+                        continue
+                    word_ids = self.word_to_ids(words.split("\002"))
+                    label_ids = self.label_to_ids(labels.split("\002"))
+                    assert len(word_ids) == len(label_ids)
+                    words_len = np.int64(len(word_ids))
+                        
+                    word_ids = word_ids[0:max_seq_len]
+                    words_len = np.int64(len(word_ids))
+                    word_ids += [0 for _ in range(max_seq_len - words_len)]
+                    label_ids = label_ids[0:max_seq_len]
+                    label_ids += [0 for _ in range(max_seq_len - words_len)]
+                    assert len(word_ids) == len(label_ids)
+                    yield word_ids, label_ids, words_len
+            elif mode == "test": 
+                headline = next(fread)
+                headline = headline.strip().split('\t')
+                assert len(headline) == 2 and headline[0] == "text_a" and headline[
+                           1] == "label"
+                buf = []
+                for line in fread:
+                    words, labels = line.strip("\n").split("\t")
+                    if len(words) < 1:
+                        continue
+                    word_ids = self.word_to_ids(words.split("\002"))
+                    label_ids = self.label_to_ids(labels.split("\002"))
+                    assert len(word_ids) == len(label_ids)
+                    words_len = np.int64(len(word_ids))
+                    yield word_ids, label_ids, words_len
+            else: 
+                for line in fread: 
+                    words = line.strip("\n").split('\t')[0]
+                    if words == u"text_a": 
+                        continue
+                    if "\002" not in words: 
+                        word_ids = self.word_to_ids(words)
+                    else: 
+                        word_ids = self.word_to_ids(words.split("\002"))
+                    words_len = np.int64(len(word_ids))
+                    yield word_ids, words_len
+
+            fread.close()
+
+        return wrapper
+
+
+def create_lexnet_data_generator(args, reader, file_name, place, mode="train"): 
+    def padding_data(max_len, batch_data): 
+        padding_batch_data = []
+        for data in batch_data: 
+            data += [0 for _ in range(max_len - len(data))]
+            padding_batch_data.append(data)
+        return padding_batch_data
+
+    def wrapper(): 
+        if mode == "train": 
+            batch_words, batch_labels, seq_lens = [], [], []
+            for epoch in xrange(args.epoch):
+                for instance in reader.file_reader(
+                        file_name, mode, max_seq_len=args.max_seq_len)():
+                    words, labels, words_len = instance
+                    if len(seq_lens) < args.batch_size:
+                        batch_words.append(words)
+                        batch_labels.append(labels)
+                        seq_lens.append(words_len)
+                    if len(seq_lens) == args.batch_size: 
+                        yield batch_words, seq_lens, batch_labels, batch_labels
+                        batch_words, batch_labels, seq_lens = [], [], []
+
+            if len(seq_lens) > 0:
+                yield batch_words, seq_lens, batch_labels, batch_labels
+        elif mode == "test": 
+            batch_words, batch_labels, seq_lens, max_len = [], [], [], 0
+            for instance in reader.file_reader(
+                file_name, mode, max_seq_len=args.max_seq_len)():
+                words, labels, words_len = instance
+                max_len = words_len if words_len > max_len else max_len
+                if len(seq_lens) < args.batch_size:
+                    batch_words.append(words)
+                    seq_lens.append(words_len)
+                    batch_labels.append(labels)
+                if len(seq_lens) == args.batch_size: 
+                    padding_batch_words = padding_data(max_len, batch_words)
+                    padding_batch_labels = padding_data(max_len, batch_labels)
+                    yield padding_batch_words, seq_lens, padding_batch_labels, padding_batch_labels
+                    batch_words, batch_labels, seq_lens, max_len = [], [], [], 0
+            if len(seq_lens) > 0: 
+                padding_batch_words = padding_data(max_len, batch_words)
+                padding_batch_labels = padding_data(max_len, batch_labels)
+                yield padding_batch_words, seq_lens, padding_batch_labels, padding_batch_labels
+
+        else: 
+            batch_words, seq_lens, max_len = [], [], 0
+            for instance in reader.file_reader(
+                   file_name, mode, max_seq_len=args.max_seq_len)():
+                words, words_len = instance
+                if len(seq_lens) < args.batch_size:
+                    batch_words.append(words)
+                    seq_lens.append(words_len)
+                    max_len = words_len if words_len > max_len else max_len
+                if len(seq_lens) == args.batch_size: 
+                    padding_batch_words = padding_data(max_len, batch_words)
+                    yield padding_batch_words, seq_lens
+                    batch_words, seq_lens, max_len = [], [], 0
+            if len(seq_lens) > 0: 
+                padding_batch_words = padding_data(max_len, batch_words)
+                yield padding_batch_words, seq_lens
+
+    return wrapper
+
+
+def create_dataloader(generator, place, feed_list=None):
+    if not feed_list:
+        data_loader = paddle.io.DataLoader.from_generator(
+            capacity=50,
+            use_double_buffer=True,
+            iterable=True,
+            return_list=True)
+    else:
+        data_loader = paddle.io.DataLoader.from_generator(
+            feed_list=feed_list,
+            capacity=50,
+            use_double_buffer=True,
+            iterable=True,
+            return_list=True)
+    data_loader.set_batch_generator(generator, places=place)
+    return data_loader
+
+

examples/sequence_tagging/sequence_tagging.yaml

+word_dict_path: "./conf/word.dic"
+label_dict_path: "./conf/tag.dic"
+word_rep_dict_path: "./conf/q2b.dic"
+device: "cpu"
+dynamic: True
+epoch: 10
+base_learning_rate: 0.001
+word_emb_dim: 128
+grnn_hidden_dim: 128
+bigru_num: 2
+emb_learning_rate: 1.0
+crf_learning_rate: 1.0
+batch_size: 300
+max_seq_len: 126
+num_devices: 1
+save_dir: "model"
+init_from_checkpoint: "model_baseline/params"
+init_from_pretrain_model: ""
+save_freq: 1
+eval_freq: 1
+output_file: "predict.result"
+test_file: "./data/test.tsv"
+train_file: "./data/train.tsv"
+predict_file: "./data/infer.tsv"
+mode: "train"

examples/sequence_tagging/train.py

+#   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.
+"""
+SequenceTagging network structure
+"""
+
+from __future__ import division
+from __future__ import print_function
+
+import io
+import os
+import sys
+import math
+import argparse
+import numpy as np
+
+work_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+sys.path.append(os.path.join(work_dir, "../"))
+
+
+from hapi.metrics import Metric
+from hapi.model import Model, Input, Loss, set_device
+from hapi.text.text import SequenceTagging
+
+from utils.check import check_gpu, check_version
+from utils.configure import PDConfig
+from reader import LacDataset, create_lexnet_data_generator, create_dataloader 
+
+import paddle.fluid as fluid
+from paddle.fluid.optimizer import AdamOptimizer
+
+
+class SeqTagging(Model):
+    def __init__(self, args, vocab_size, num_labels, length=None):
+        super(SeqTagging, self).__init__()
+        """
+        define the lexical analysis network structure
+        word: stores the input of the model
+        for_infer: a boolean value, indicating if the model to be created is for training or predicting.
+
+        return:
+            for infer: return the prediction
+            otherwise: return the prediction
+        """
+        self.mode_type = args.mode
+        self.word_emb_dim = args.word_emb_dim
+        self.vocab_size = vocab_size
+        self.num_labels = num_labels
+        self.grnn_hidden_dim = args.grnn_hidden_dim
+        self.emb_lr = args.emb_learning_rate if 'emb_learning_rate' in dir(
+            args) else 1.0
+        self.crf_lr = args.emb_learning_rate if 'crf_learning_rate' in dir(
+            args) else 1.0
+        self.bigru_num = args.bigru_num
+        self.batch_size = args.batch_size
+        self.init_bound = 0.1
+        self.length=length
+
+        self.sequence_tagging = SequenceTagging(
+                        vocab_size=self.vocab_size,
+                        num_labels=self.num_labels,
+                        batch_size=self.batch_size,
+                        word_emb_dim=self.word_emb_dim,
+                        grnn_hidden_dim=self.grnn_hidden_dim,
+                        emb_learning_rate=self.emb_lr,
+                        crf_learning_rate=self.crf_lr,
+                        bigru_num=self.bigru_num,
+                        init_bound=self.init_bound,
+                        length=self.length)
+
+    def forward(self, *inputs):
+        """
+        Configure the network
+        """
+        word = inputs[0]
+        lengths = inputs[1]
+        if self.mode_type == "train" or self.mode_type == "test": 
+            target = inputs[2]
+            outputs = self.sequence_tagging(word, lengths, target)
+        else: 
+            outputs = self.sequence_tagging(word, lengths)
+        return outputs
+
+
+class Chunk_eval(fluid.dygraph.Layer):
+    def __init__(self,
+                 num_chunk_types,
+                 chunk_scheme,
+                 excluded_chunk_types=None):
+        super(Chunk_eval, self).__init__()
+        self.num_chunk_types = num_chunk_types
+        self.chunk_scheme = chunk_scheme
+        self.excluded_chunk_types = excluded_chunk_types
+
+    def forward(self, input, label, seq_length=None):
+        precision = self._helper.create_variable_for_type_inference(
+            dtype="float32")
+        recall = self._helper.create_variable_for_type_inference(
+            dtype="float32")
+        f1_score = self._helper.create_variable_for_type_inference(
+            dtype="float32")
+        num_infer_chunks = self._helper.create_variable_for_type_inference(
+            dtype="int64")
+        num_label_chunks = self._helper.create_variable_for_type_inference(
+            dtype="int64")
+        num_correct_chunks = self._helper.create_variable_for_type_inference(
+            dtype="int64")
+        this_input = {"Inference": input, "Label": label}
+        if seq_length is not None:
+            this_input["SeqLength"] = seq_length
+        self._helper.append_op(
+            type='chunk_eval',
+            inputs=this_input,
+            outputs={
+                "Precision": [precision],
+                "Recall": [recall],
+                "F1-Score": [f1_score],
+                "NumInferChunks": [num_infer_chunks],
+                "NumLabelChunks": [num_label_chunks],
+                "NumCorrectChunks": [num_correct_chunks]
+            },
+            attrs={
+                "num_chunk_types": self.num_chunk_types,
+                "chunk_scheme": self.chunk_scheme,
+                "excluded_chunk_types": self.excluded_chunk_types or []
+            })
+        return (num_infer_chunks, num_label_chunks, num_correct_chunks)
+
+
+class LacLoss(Loss):
+    def __init__(self):
+        super(LacLoss, self).__init__()
+        pass
+
+    def forward(self, outputs, labels):
+        avg_cost = outputs[1]
+        return avg_cost
+
+
+class ChunkEval(Metric):
+    def __init__(self, num_labels, name=None, *args, **kwargs):
+        super(ChunkEval, self).__init__(*args, **kwargs)
+        self._init_name(name)
+        self.chunk_eval = Chunk_eval(
+            int(math.ceil((num_labels - 1) / 2.0)), "IOB")
+        self.reset()
+
+    def add_metric_op(self, *args): 
+        crf_decode = args[0]
+        lengths = args[2]
+        label = args[3]
+        (num_infer_chunks, num_label_chunks,
+         num_correct_chunks) = self.chunk_eval(
+             input=crf_decode, label=label, seq_length=lengths)
+        return [num_infer_chunks, num_label_chunks, num_correct_chunks]
+
+    def update(self, num_infer_chunks, num_label_chunks, num_correct_chunks,
+               *args, **kwargs):
+        self.infer_chunks_total += num_infer_chunks
+        self.label_chunks_total += num_label_chunks
+        self.correct_chunks_total += num_correct_chunks
+        precision = float(
+            num_correct_chunks) / num_infer_chunks if num_infer_chunks else 0
+        recall = float(
+            num_correct_chunks) / num_label_chunks if num_label_chunks else 0
+        f1_score = float(2 * precision * recall) / (
+            precision + recall) if num_correct_chunks else 0
+        return [precision, recall, f1_score]
+
+    def reset(self):
+        self.infer_chunks_total = 0
+        self.label_chunks_total = 0
+        self.correct_chunks_total = 0
+
+    def accumulate(self):
+        precision = float(
+            self.correct_chunks_total
+        ) / self.infer_chunks_total if self.infer_chunks_total else 0
+        recall = float(
+            self.correct_chunks_total
+        ) / self.label_chunks_total if self.label_chunks_total else 0
+        f1_score = float(2 * precision * recall) / (
+            precision + recall) if self.correct_chunks_total else 0
+        res = [precision, recall, f1_score]
+        return res
+
+    def _init_name(self, name):
+        name = name or 'chunk eval'
+        self._name = ['precision', 'recall', 'F1']
+
+    def name(self):
+        return self._name
+
+
+def main(args):
+    place = set_device(args.device)
+    fluid.enable_dygraph(place) if args.dynamic else None
+
+    inputs = [Input([None, None], 'int64', name='words'),
+              Input([None], 'int64', name='length'), 
+              Input([None, None], 'int64', name='target')]
+
+    labels = [Input([None, None], 'int64', name='labels')]
+
+    feed_list = None if args.dynamic else [x.forward() for x in inputs + labels]
+    dataset = LacDataset(args)
+    train_path = args.train_file
+    test_path = args.test_file
+
+    train_generator = create_lexnet_data_generator(
+        args, reader=dataset, file_name=train_path, place=place, mode="train")
+    test_generator = create_lexnet_data_generator(
+        args, reader=dataset, file_name=test_path, place=place, mode="test")
+
+    train_dataset = create_dataloader(
+        train_generator, place, feed_list=feed_list)
+    test_dataset = create_dataloader(
+        test_generator, place, feed_list=feed_list)
+
+    vocab_size = dataset.vocab_size
+    num_labels = dataset.num_labels
+    model = SeqTagging(args, vocab_size, num_labels)
+
+    optim = AdamOptimizer(
+        learning_rate=args.base_learning_rate,
+        parameter_list=model.parameters())
+
+    model.prepare(
+        optim,
+        LacLoss(),
+        ChunkEval(num_labels),
+        inputs=inputs,
+        labels=labels,
+        device=args.device)
+
+    if args.init_from_checkpoint:
+        model.load(args.init_from_checkpoint)
+
+    if args.init_from_pretrain_model:
+        model.load(args.init_from_pretrain_model, reset_optimizer=True)
+
+    model.fit(train_dataset,
+              test_dataset,
+              epochs=args.epoch,
+              batch_size=args.batch_size,
+              eval_freq=args.eval_freq,
+              save_freq=args.save_freq,
+              save_dir=args.save_dir)
+
+
+if __name__ == '__main__':
+    args = PDConfig(yaml_file="sequence_tagging.yaml")
+    args.build()
+    args.Print()
+    
+    use_gpu = True if args.device == "gpu" else False
+    check_gpu(use_gpu)
+    check_version()
+
+    main(args)

examples/sequence_tagging/utils/check.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import sys
+
+import paddle.fluid as fluid
+
+__all__ = ['check_gpu', 'check_version']
+
+
+def check_gpu(use_gpu):
+    """
+     Log error and exit when set use_gpu=true in paddlepaddle
+     cpu version.
+     """
+    err = "Config use_gpu cannot be set as true while you are " \
+          "using paddlepaddle cpu version ! \nPlease try: \n" \
+          "\t1. Install paddlepaddle-gpu to run model on GPU \n" \
+          "\t2. Set use_gpu as false in config file to run " \
+          "model on CPU"
+
+    try:
+        if use_gpu and not fluid.is_compiled_with_cuda():
+            print(err)
+            sys.exit(1)
+    except Exception as e:
+        pass
+
+
+def check_version():
+    """
+    Log error and exit when the installed version of paddlepaddle is
+    not satisfied.
+    """
+    err = "PaddlePaddle version 1.6 or higher is required, " \
+          "or a suitable develop version is satisfied as well. \n" \
+          "Please make sure the version is good with your code." \
+
+    try:
+        fluid.require_version('1.7.0')
+    except Exception as e:
+        print(err)
+        sys.exit(1)

examples/sequence_tagging/utils/configure.py

+# 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 __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import sys
+import argparse
+import json
+import yaml
+import six
+import logging
+
+logging_only_message = "%(message)s"
+logging_details = "%(asctime)s.%(msecs)03d %(levelname)s %(module)s - %(funcName)s: %(message)s"
+
+
+class JsonConfig(object):
+    """
+    A high-level api for handling json configure file.
+    """
+
+    def __init__(self, config_path):
+        self._config_dict = self._parse(config_path)
+
+    def _parse(self, config_path):
+        try:
+            with open(config_path) as json_file:
+                config_dict = json.load(json_file)
+        except:
+            raise IOError("Error in parsing bert model config file '%s'" %
+                          config_path)
+        else:
+            return config_dict
+
+    def __getitem__(self, key):
+        return self._config_dict[key]
+
+    def print_config(self):
+        for arg, value in sorted(six.iteritems(self._config_dict)):
+            print('%s: %s' % (arg, value))
+        print('------------------------------------------------')
+
+
+class ArgumentGroup(object):
+    def __init__(self, parser, title, des):
+        self._group = parser.add_argument_group(title=title, description=des)
+
+    def add_arg(self, name, type, default, help, **kwargs):
+        type = str2bool if type == bool else type
+        self._group.add_argument(
+            "--" + name,
+            default=default,
+            type=type,
+            help=help + ' Default: %(default)s.',
+            **kwargs)
+
+
+class ArgConfig(object):
+    """
+    A high-level api for handling argument configs.
+    """
+
+    def __init__(self):
+        parser = argparse.ArgumentParser()
+
+        train_g = ArgumentGroup(parser, "training", "training options.")
+        train_g.add_arg("epoch", int, 3, "Number of epoches for fine-tuning.")
+        train_g.add_arg("learning_rate", float, 5e-5,
+                        "Learning rate used to train with warmup.")
+        train_g.add_arg(
+            "lr_scheduler",
+            str,
+            "linear_warmup_decay",
+            "scheduler of learning rate.",
+            choices=['linear_warmup_decay', 'noam_decay'])
+        train_g.add_arg("weight_decay", float, 0.01,
+                        "Weight decay rate for L2 regularizer.")
+        train_g.add_arg(
+            "warmup_proportion", float, 0.1,
+            "Proportion of training steps to perform linear learning rate warmup for."
+        )
+        train_g.add_arg("save_steps", int, 1000,
+                        "The steps interval to save checkpoints.")
+        train_g.add_arg("use_fp16", bool, False,
+                        "Whether to use fp16 mixed precision training.")
+        train_g.add_arg(
+            "loss_scaling", float, 1.0,
+            "Loss scaling factor for mixed precision training, only valid when use_fp16 is enabled."
+        )
+        train_g.add_arg("pred_dir", str, None,
+                        "Path to save the prediction results")
+
+        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", bool, False, "Whether to output verbose log.")
+
+        run_type_g = ArgumentGroup(parser, "run_type", "running type options.")
+        run_type_g.add_arg("use_cuda", bool, True,
+                           "If set, use GPU for training.")
+        run_type_g.add_arg(
+            "use_fast_executor", bool, False,
+            "If set, use fast parallel executor (in experiment).")
+        run_type_g.add_arg(
+            "num_iteration_per_drop_scope", int, 1,
+            "Ihe iteration intervals to clean up temporary variables.")
+        run_type_g.add_arg("do_train", bool, True,
+                           "Whether to perform training.")
+        run_type_g.add_arg("do_predict", bool, True,
+                           "Whether to perform prediction.")
+
+        custom_g = ArgumentGroup(parser, "customize", "customized options.")
+
+        self.custom_g = custom_g
+
+        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 str2bool(v):
+    # because argparse does not support to parse "true, False" as python
+    # boolean directly
+    return v.lower() in ("true", "t", "1")
+
+
+def print_arguments(args, log=None):
+    if not log:
+        print('-----------  Configuration Arguments -----------')
+        for arg, value in sorted(six.iteritems(vars(args))):
+            print('%s: %s' % (arg, value))
+        print('------------------------------------------------')
+    else:
+        log.info('-----------  Configuration Arguments -----------')
+        for arg, value in sorted(six.iteritems(vars(args))):
+            log.info('%s: %s' % (arg, value))
+        log.info('------------------------------------------------')
+
+
+class PDConfig(object):
+    """
+    A high-level API for managing configuration files in PaddlePaddle.
+    Can jointly work with command-line-arugment, json files and yaml files.
+    """
+
+    def __init__(self, json_file="", yaml_file="", fuse_args=True):
+        """
+            Init funciton for PDConfig.
+            json_file: the path to the json configure file.
+            yaml_file: the path to the yaml configure file.
+            fuse_args: if fuse the json/yaml configs with argparse.
+        """
+        assert isinstance(json_file, str)
+        assert isinstance(yaml_file, str)
+
+        if json_file != "" and yaml_file != "":
+            raise Warning(
+                "json_file and yaml_file can not co-exist for now. please only use one configure file type."
+            )
+            return
+
+        self.args = None
+        self.arg_config = {}
+        self.json_config = {}
+        self.yaml_config = {}
+
+        parser = argparse.ArgumentParser()
+
+        self.default_g = ArgumentGroup(parser, "default", "default options.")
+        self.yaml_g = ArgumentGroup(parser, "yaml", "options from yaml.")
+        self.json_g = ArgumentGroup(parser, "json", "options from json.")
+        self.com_g = ArgumentGroup(parser, "custom", "customized options.")
+
+        self.default_g.add_arg("do_train", bool, False,
+                               "Whether to perform training.")
+        self.default_g.add_arg("do_predict", bool, False,
+                               "Whether to perform predicting.")
+        self.default_g.add_arg("do_eval", bool, False,
+                               "Whether to perform evaluating.")
+        self.default_g.add_arg("do_save_inference_model", bool, False,
+                               "Whether to perform model saving for inference.")
+
+        # NOTE: args for profiler
+        self.default_g.add_arg("is_profiler", int, 0, "the switch of profiler tools. (used for benchmark)")
+        self.default_g.add_arg("profiler_path", str, './', "the profiler output file path. (used for benchmark)")
+        self.default_g.add_arg("max_iter", int, 0, "the max train batch num.(used for benchmark)")
+
+        self.parser = parser
+
+        if json_file != "":
+            self.load_json(json_file, fuse_args=fuse_args)
+
+        if yaml_file:
+            self.load_yaml(yaml_file, fuse_args=fuse_args)
+
+    def load_json(self, file_path, fuse_args=True):
+
+        if not os.path.exists(file_path):
+            raise Warning("the json file %s does not exist." % file_path)
+            return
+
+        with open(file_path, "r") as fin:
+            self.json_config = json.loads(fin.read())
+            fin.close()
+
+        if fuse_args:
+            for name in self.json_config:
+                if isinstance(self.json_config[name], list):
+                    self.json_g.add_arg(
+                        name,
+                        type(self.json_config[name][0]),
+                        self.json_config[name],
+                        "This is from %s" % file_path,
+                        nargs=len(self.json_config[name]))
+                    continue
+                if not isinstance(self.json_config[name], int) \
+                    and not isinstance(self.json_config[name], float) \
+                    and not isinstance(self.json_config[name], str) \
+                    and not isinstance(self.json_config[name], bool):
+
+                    continue
+
+                self.json_g.add_arg(name,
+                                    type(self.json_config[name]),
+                                    self.json_config[name],
+                                    "This is from %s" % file_path)
+
+    def load_yaml(self, file_path, fuse_args=True):
+
+        if not os.path.exists(file_path):
+            raise Warning("the yaml file %s does not exist." % file_path)
+            return
+
+        with open(file_path, "r") as fin:
+            self.yaml_config = yaml.load(fin, Loader=yaml.SafeLoader)
+            fin.close()
+
+        if fuse_args:
+            for name in self.yaml_config:
+                if isinstance(self.yaml_config[name], list):
+                    self.yaml_g.add_arg(
+                        name,
+                        type(self.yaml_config[name][0]),
+                        self.yaml_config[name],
+                        "This is from %s" % file_path,
+                        nargs=len(self.yaml_config[name]))
+                    continue
+
+                if not isinstance(self.yaml_config[name], int) \
+                    and not isinstance(self.yaml_config[name], float) \
+                    and not isinstance(self.yaml_config[name], str) \
+                    and not isinstance(self.yaml_config[name], bool):
+
+                    continue
+
+                self.yaml_g.add_arg(name,
+                                    type(self.yaml_config[name]),
+                                    self.yaml_config[name],
+                                    "This is from %s" % file_path)
+
+    def build(self):
+        self.args = self.parser.parse_args()
+        self.arg_config = vars(self.args)
+
+    def __add__(self, new_arg):
+        assert isinstance(new_arg, list) or isinstance(new_arg, tuple)
+        assert len(new_arg) >= 3
+        assert self.args is None
+
+        name = new_arg[0]
+        dtype = new_arg[1]
+        dvalue = new_arg[2]
+        desc = new_arg[3] if len(
+            new_arg) == 4 else "Description is not provided."
+
+        self.com_g.add_arg(name, dtype, dvalue, desc)
+
+        return self
+
+    def __getattr__(self, name):
+        if name in self.arg_config:
+            return self.arg_config[name]
+
+        if name in self.json_config:
+            return self.json_config[name]
+
+        if name in self.yaml_config:
+            return self.yaml_config[name]
+
+        raise Warning("The argument %s is not defined." % name)
+
+    def Print(self):
+
+        print("-" * 70)
+        for name in self.arg_config:
+            print("%s:\t\t\t\t%s" % (str(name), str(self.arg_config[name])))
+
+        for name in self.json_config:
+            if name not in self.arg_config:
+                print("%s:\t\t\t\t%s" %
+                      (str(name), str(self.json_config[name])))
+
+        for name in self.yaml_config:
+            if name not in self.arg_config:
+                print("%s:\t\t\t\t%s" %
+                      (str(name), str(self.yaml_config[name])))
+
+        print("-" * 70)
+
+
+if __name__ == "__main__":
+    """
+    pd_config = PDConfig(json_file = "./test/bert_config.json")
+    pd_config.build()
+
+    print(pd_config.do_train)
+    print(pd_config.hidden_size)
+
+    pd_config = PDConfig(yaml_file = "./test/bert_config.yaml")
+    pd_config.build()
+
+    print(pd_config.do_train)
+    print(pd_config.hidden_size)
+    """
+
+    pd_config = PDConfig(yaml_file="./test/bert_config.yaml")
+    pd_config += ("my_age", int, 18, "I am forever 18.")
+    pd_config.build()
+
+    print(pd_config.do_train)
+    print(pd_config.hidden_size)
+    print(pd_config.my_age)

examples/sequence_tagging/utils/metrics.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import sys
+
+import paddle.fluid as fluid
+
+__all__ = ['chunk_count', "build_chunk"]
+
+
+def build_chunk(data_list, id2label_dict): 
+    """
+    Assembly entity
+    """
+    tag_list = [id2label_dict.get(str(id)) for id in data_list]
+    ner_dict = {}
+    ner_str = ""
+    ner_start = 0
+    for i in range(len(tag_list)): 
+        tag = tag_list[i]
+        if tag == u"O": 
+            if i != 0: 
+                key = "%d_%d" % (ner_start, i - 1)
+                ner_dict[key] = ner_str
+            ner_start = i
+            ner_str = tag 
+        elif tag.endswith(u"B"): 
+            if i != 0: 
+                key = "%d_%d" % (ner_start, i - 1)
+                ner_dict[key] = ner_str
+            ner_start = i
+            ner_str = tag.split('-')[0]
+        elif tag.endswith(u"I"): 
+            if tag.split('-')[0] != ner_str: 
+                if i != 0: 
+                    key = "%d_%d" % (ner_start, i - 1)
+                    ner_dict[key] = ner_str
+                ner_start = i
+                ner_str = tag.split('-')[0]
+    return ner_dict
+                    
+
+def chunk_count(infer_numpy, label_numpy, seq_len, id2label_dict):
+    """
+    calculate num_correct_chunks num_error_chunks total_num for metrics
+    """
+    num_infer_chunks, num_label_chunks, num_correct_chunks = 0, 0, 0
+    assert infer_numpy.shape[0] == label_numpy.shape[0]
+
+    for i in range(infer_numpy.shape[0]): 
+        infer_list = infer_numpy[i][: seq_len[i]]
+        label_list = label_numpy[i][: seq_len[i]]
+        infer_dict = build_chunk(infer_list, id2label_dict)
+        num_infer_chunks += len(infer_dict)
+        label_dict = build_chunk(label_list, id2label_dict)
+        num_label_chunks += len(label_dict)
+        for key in infer_dict: 
+            if key in label_dict and label_dict[key] == infer_dict[key]: 
+                num_correct_chunks += 1
+    return num_infer_chunks, num_label_chunks, num_correct_chunks
+

tsm/infer.py → examples/tsm/infer.py

@@ -19,10 +19,10 @@ import os
 import argparse
 import numpy as np
 
-from model import Input, set_device
-from models import tsm_resnet50
+from hapi.model import Input, set_device
 
 from check import check_gpu, check_version
+from modeling import tsm_resnet50
 from kinetics_dataset import KineticsDataset
 from transforms import *
 

tsm/kinetics_dataset.py → examples/tsm/kinetics_dataset.py

@@ -26,7 +26,7 @@ except ImportError:
     import pickle
     from io import BytesIO
 
-from paddle.fluid.io import Dataset
+from paddle.io import Dataset
 
 import logging
 logger = logging.getLogger(__name__)
@@ -100,19 +100,12 @@ class KineticsDataset(Dataset):
     def __getitem__(self, idx):
         pickle_path = os.path.join(self.pickle_dir, self.pickle_paths[idx])
 
-        try:
-            if six.PY2:
-                data = pickle.load(open(pickle_path, 'rb'))
-            else:
-                data = pickle.load(open(pickle_path, 'rb'), encoding='bytes')
-
-            vid, label, frames = data
-            if len(frames) < 1:
-                logger.error("{} contains no frame".format(pickle_path))
-                sys.exit(-1)
-        except Exception as e:
-            logger.error("Load {} failed: {}".format(pickle_path, e))
-            sys.exit(-1)
+        if six.PY2:
+            data = pickle.load(open(pickle_path, 'rb'))
+        else:
+            data = pickle.load(open(pickle_path, 'rb'), encoding='bytes')
+
+        vid, label, frames = data
 
         if self.label_list is not None:
             label = self.label_list.index(label)

tsm/main.py → examples/tsm/main.py

@@ -22,10 +22,10 @@ import numpy as np
 from paddle import fluid
 from paddle.fluid.dygraph.parallel import ParallelEnv
 
-from model import Model, CrossEntropy, Input, set_device
-from metrics import Accuracy
-from models import tsm_resnet50
+from hapi.model import Model, CrossEntropy, Input, set_device
+from hapi.metrics import Accuracy
 
+from modeling import tsm_resnet50
 from check import check_gpu, check_version
 from kinetics_dataset import KineticsDataset
 from transforms import *

models/tsm.py → examples/tsm/modeling.py

@@ -17,8 +17,8 @@ import paddle.fluid as fluid
 from paddle.fluid.layer_helper import LayerHelper
 from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear
 
-from model import Model
-from .download import get_weights_path
+from hapi.model import Model
+from hapi.download import get_weights_path
 
 __all__ = ["TSM_ResNet", "tsm_resnet50"]
 
@@ -196,9 +196,17 @@ def _tsm_resnet(num_layers, seg_num=8, num_classes=400, pretrained=True):
         weight_path = get_weights_path(*(pretrain_infos[num_layers]))
         assert weight_path.endswith('.pdparams'), \
                 "suffix of weight must be .pdparams"
-        model.load(weight_path[:-9])
+        model.load(weight_path)
     return model
 
 
 def tsm_resnet50(seg_num=8, num_classes=400, pretrained=True):
+    """TSM model with 50-layer ResNet as backbone
+    
+    Args:
+        seg_num (int): segment number of each video sample. Default 8.
+        num_classes (int): video class number. Default 400.
+        pretrained (bool): If True, returns a model with pre-trained model
+            on COCO, default True
+    """
     return _tsm_resnet(50, seg_num, num_classes, pretrained)

examples/yolov3/.gitignore

+dataset/voc*
+pretrain_weights/darknet53_pretrained.pdparams

yolov3/README.md → examples/yolov3/README.md

@@ -101,11 +101,10 @@ YOLOv3 的网络结构由基础特征提取网络、multi-scale特征融合层
 
 ### 模型训练
 
-数据准备完毕后，可使用`main.py`脚本启动训练和评估，如下脚本会自动每epoch交替进行训练和模型评估，并将checkpoint默认保存在`yolo_checkpoint`目录下。
+数据准备完成后，可使用`main.py`脚本启动训练和评估，如下脚本会自动每epoch交替进行训练和模型评估，并将checkpoint默认保存在`yolo_checkpoint`目录下。
 
 YOLOv3模型训练总batch_size为64训练，以下以使用4卡Tesla P40每卡batch_size为16训练介绍训练方式。对于静态图和动态图，多卡训练中`--batch_size`为每卡上的batch_size，即总batch_size为`--batch_size`乘以卡数。
 
-
 `main.py`脚本参数可通过如下命令查询
 
 ```bash

yolov3/coco.py → examples/yolov3/coco.py

@@ -18,9 +18,8 @@ from __future__ import print_function
 import os
 import cv2
 import numpy as np
-from pycocotools.coco import COCO
 
-from paddle.fluid.io import Dataset
+from paddle.io import Dataset
 
 import logging
 logger = logging.getLogger(__name__)
@@ -91,6 +90,7 @@ class COCODataset(Dataset):
         self._load_roidb_and_cname2cid()
 
     def _load_roidb_and_cname2cid(self):
+        from pycocotools.coco import COCO
         assert self._anno_path.endswith('.json'), \
             'invalid coco annotation file: ' + anno_path
         coco = COCO(self._anno_path)
@@ -186,30 +186,31 @@ class COCODataset(Dataset):
             data = np.frombuffer(f.read(), dtype='uint8')
             im = cv2.imdecode(data, 1)
             im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
-        im_info = np.array([roidb['im_id'][0], roidb['h'], roidb['w']], dtype='int32')
+        im_id = roidb['im_id']
+        im_shape = np.array([roidb['h'], roidb['w']], dtype='int32')
         gt_bbox = roidb['gt_bbox']
         gt_class = roidb['gt_class']
         gt_score = roidb['gt_score']
-        return im_info, im, gt_bbox, gt_class, gt_score
+        return im_id, im_shape, im, gt_bbox, gt_class, gt_score
 
     def __getitem__(self, idx):
-        im_info, im, gt_bbox, gt_class, gt_score = self._getitem_by_index(idx)
+        im_id, im_shape, im, gt_bbox, gt_class, gt_score = self._getitem_by_index(idx)
 
         if self._mixup:
             mixup_idx = idx + np.random.randint(1, self.__len__())
             mixup_idx %= self.__len__()
-            _, mixup_im, mixup_bbox, mixup_class, _ = \
+            _, _, mixup_im, mixup_bbox, mixup_class, _ = \
                             self._getitem_by_index(mixup_idx)
             
-            im, gt_bbox, gt_class, gt_score = \
+            im_shape, im, gt_bbox, gt_class, gt_score = \
                     self._mixup_image(im, gt_bbox, gt_class, mixup_im,
                                       mixup_bbox, mixup_class)
 
         if self._transform:
-            im_info, im, gt_bbox, gt_class, gt_score = \
-                    self._transform(im_info, im, gt_bbox, gt_class, gt_score)
+            im_id, im_shape, im, gt_bbox, gt_class, gt_score = \
+                self._transform(im_id, im_shape, im, gt_bbox, gt_class, gt_score)
 
-        return [im_info, im, gt_bbox, gt_class, gt_score]
+        return [im_id, im_shape, im, gt_bbox, gt_class, gt_score]
 
     def _mixup_image(self, img1, bbox1, class1, img2, bbox2, class2):
         factor = np.random.beta(self._alpha, self._beta)
@@ -234,7 +235,9 @@ class COCODataset(Dataset):
         score2 = np.ones_like(class2, dtype="float32") * (1.0 - factor)
         gt_score = np.concatenate((score1, score2), axis=0)
 
-        return img, gt_bbox, gt_class, gt_score
+        im_shape = np.array([h, w], dtype='int32')
+
+        return im_shape, img, gt_bbox, gt_class, gt_score
     
     @property
     def mixup(self):

yolov3/dataset/download_voc.py → examples/yolov3/dataset/download_voc.py

@@ -17,7 +17,7 @@ import os.path as osp
 import sys
 import tarfile
 
-from models.download import _download
+from hapi.download import _download
 
 import logging
 logger = logging.getLogger(__name__)

callbacks.py → hapi/callbacks.py

@@ -15,7 +15,7 @@
 import six
 import copy
 
-from progressbar import ProgressBar
+from .progressbar import ProgressBar
 from paddle.fluid.dygraph.parallel import ParallelEnv
 
 
@@ -218,8 +218,6 @@ class ProgBarLogger(Callback):
             # if steps is not None, last step will update in on_epoch_end
             if self.steps and self.train_step < self.steps:
                 self._updates(logs, 'train')
-            else:
-                self._updates(logs, 'train')
 
     def on_epoch_end(self, epoch, logs=None):
         logs = logs or {}
@@ -238,7 +236,7 @@ class ProgBarLogger(Callback):
 
     def on_eval_batch_end(self, step, logs=None):
         logs = logs or {}
-        self.eval_step = step
+        self.eval_step += 1
         samples = logs.get('batch_size', 1)
         self.evaled_samples += samples