merge conflict solve

README.md

@@ -110,6 +110,6 @@ fastNLP的大致工作流程如上图所示，而项目结构如下：
 </table>
 
 
-
+<hr>
 
 *In memory of @FengZiYjun.  May his soul rest in peace. We will miss you very very much!*

docs/source/index.rst

@@ -55,6 +55,7 @@ fastNLP 在 :mod:`~fastNLP.models` 模块中内置了如 :class:`~fastNLP.models
     安装指南 <user/installation>
     快速入门 <user/quickstart>
     详细指南 <user/tutorial_one>
+    科研指南 <user/with_fitlog>
 
 API 文档
 -------------

docs/source/user/with_fitlog.rst

@@ -2,4 +2,121 @@
 科研向导
 =================
 
-本文介绍使用 fastNLP 和 fitlog 进行科学研究的方法
+本文介绍结合使用 fastNLP 和 fitlog 进行科研的方法。
+
+首先，我们需要安装 `fitlog <https://fitlog.readthedocs.io/>`_ 。你需要确认你的电脑中没有其它名为 `fitlog` 的命令。
+
+我们从命令行中进入到一个文件夹，现在我们要在文件夹中创建我们的 fastNLP 项目。你可以在命令行输入 `fitlog init test1` ，
+然后你会看到如下提示::
+
+    Initialized empty Git repository in /Users/fdujyn/workspaces/test1/.git/
+    Auto commit by fitlog
+    Initialized empty Git repository in /Users/fdujyn/workspaces/test1/.git/
+    Fitlog project test1 is initialized.
+
+这表明你已经创建成功了项目文件夹，并且在项目文件夹中已经初始化了 Git。如果你不想初始化 Git，
+可以参考文档 `命令行工具 <https://fitlog.readthedocs.io/zh/latest/user/command_line.html>`_
+
+现在我们进入你创建的项目文件夹 test1 中，可以看到有一个名为 logs 的文件夹，后面我们将会在里面存放你的实验记录。
+同时也有一个名为 main.py 的文件，这是我们推荐你使用的训练入口文件。文件的内容如下::
+
+    import fitlog
+
+    fitlog.commit(__file__)             # auto commit your codes
+    fitlog.add_hyper_in_file (__file__) # record your hyperparameters
+
+    """
+    Your training code here, you may use these functions to log your result:
+        fitlog.add_hyper()
+        fitlog.add_loss()
+        fitlog.add_metric()
+        fitlog.add_best_metric()
+        ......
+    """
+
+    fitlog.finish()                     # finish the logging
+
+我们推荐你保留除注释外的四行代码，它们有助于你的实验，
+他们的具体用处参见文档 `用户 API <https://fitlog.readthedocs.io/zh/latest/fitlog.html>`_
+
+我们假定你要进行前两个教程中的实验，并已经把数据复制到了项目根目录下的 tutorial_sample_dataset.csv 文件中。
+现在我们编写如下的训练代码，使用 :class:`~fastNLP.core.callback.FitlogCallback` 进行实验记录保存::
+
+    import fitlog
+    from fastNLP import Vocabulary, Trainer, CrossEntropyLoss, AccuracyMetric
+    from fastNLP.io import CSVLoader
+    from fastNLP.models import CNNText
+    from fastNLP.core.callback import FitlogCallback
+
+    fitlog.commit(__file__)             # auto commit your codes
+    fitlog.add_hyper_in_file (__file__) # record your hyperparameters
+
+    ############hyper
+    word_embed = 50
+    dropout = 0.1
+    ############hyper
+
+    loader = CSVLoader(headers=('raw_sentence', 'label'), sep='\t')
+    dataset = loader.load("tutorial_sample_dataset.csv")
+
+    dataset.apply(lambda x: x['raw_sentence'].lower(), new_field_name='sentence')
+    dataset.apply(lambda x: x['sentence'].split(), new_field_name='words', is_input=True)
+    dataset.apply(lambda x: int(x['label']), new_field_name='target', is_target=True)
+    vocab = Vocabulary(min_freq=2).from_dataset(dataset, field_name='words')
+    vocab.index_dataset(dataset, field_name='words',new_field_name='words')
+
+    model = CNNText((len(vocab),word_embed), num_classes=5, padding=2, dropout=dropout)
+
+    train_dev_data, test_data = dataset.split(0.1)
+    train_data, dev_data = train_dev_data.split(0.1)
+
+    trainer = Trainer(model=model, train_data=train_data, dev_data=dev_data,
+                      loss=CrossEntropyLoss(), metrics=AccuracyMetric(),
+                      callbacks=[FitlogCallback(test_data)])
+    trainer.train()
+
+    fitlog.finish()                     # finish the logging
+
+用命令行在项目目录下执行 `python main.py` 之后，输出结果如下::
+
+    Auto commit by fitlog
+    input fields after batch(if batch size is 2):
+        words: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2, 11])
+    target fields after batch(if batch size is 2):
+        target: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2])
+
+    training epochs started 2019-05-23-21-11-51
+    Evaluation at Epoch 1/10. Step:2/20. AccuracyMetric: acc=0.285714
+
+    Evaluation at Epoch 2/10. Step:4/20. AccuracyMetric: acc=0.285714
+
+    Evaluation at Epoch 3/10. Step:6/20. AccuracyMetric: acc=0.285714
+
+    Evaluation at Epoch 4/10. Step:8/20. AccuracyMetric: acc=0.428571
+
+    Evaluation at Epoch 5/10. Step:10/20. AccuracyMetric: acc=0.571429
+
+    Evaluation at Epoch 6/10. Step:12/20. AccuracyMetric: acc=0.571429
+
+    Evaluation at Epoch 7/10. Step:14/20. AccuracyMetric: acc=0.285714
+
+    Evaluation at Epoch 8/10. Step:16/20. AccuracyMetric: acc=0.142857
+
+    Evaluation at Epoch 9/10. Step:18/20. AccuracyMetric: acc=0.285714
+
+    Evaluation at Epoch 10/10. Step:20/20. AccuracyMetric: acc=0.571429
+
+
+    In Epoch:5/Step:10, got best dev performance:AccuracyMetric: acc=0.571429
+    Reloaded the best model.
+
+现在，我们在项目目录下输入 `fitlog log logs` ，命令行会启动一个网页，默认 url 为 ``0.0.0.0:5000`` 。
+我们在浏览器中打开网页，可以看到如下的统计表格:
+
+.. image:: ../figures/fitlogTable.png
+
+如果我们点击action中的最后一个键钮，可以看到详细的 loss 图:
+
+.. image:: ../figures/fitlogChart.png
+
+更多的教程还在编写中，敬请期待~

fastNLP/core/callback.py

@@ -54,6 +54,7 @@ __all__ = [
     "GradientClipCallback",
     "EarlyStopCallback",
     "TensorboardCallback",
+    "FitlogCallback",
     "LRScheduler",
     "ControlC",
     
@@ -65,6 +66,7 @@ import os
 
 import torch
 from copy import deepcopy
+
 try:
     from tensorboardX import SummaryWriter
     
@@ -81,6 +83,7 @@ try:
 except:
     pass
 
+
 class Callback(object):
     """
     别名：:class:`fastNLP.Callback` :class:`fastNLP.core.callback.Callback`
@@ -367,16 +370,17 @@ class GradientClipCallback(Callback):
 
     每次backward前，将parameter的gradient clip到某个范围。
 
-    :param None,torch.Tensor,List[torch.Tensor] parameters: 一般通过model.parameters()获得。如果为None则默认对Trainer
-        的model中所有参数进行clip
+    :param None,torch.Tensor,List[torch.Tensor] parameters: 一般通过model.parameters()获得。
+        如果为None则默认对Trainer的model中所有参数进行clip
     :param float clip_value: 将gradient 限制到[-clip_value, clip_value]。clip_value应该为正数
     :param str clip_type: 支持'norm', 'value'
         两种::
 
             1 'norm', 将gradient的norm rescale到[-clip_value, clip_value]
         
-            2 'value', 将gradient限制在[-clip_value, clip_value], 小于-clip_value的gradient被赋值为-clip_value;
-            大于clip_value的gradient被赋值为clip_value.
+            2 'value', 将gradient限制在[-clip_value, clip_value],
+                小于-clip_value的gradient被赋值为-clip_value;
+                大于clip_value的gradient被赋值为clip_value.
 
     """
     
@@ -431,6 +435,7 @@ class EarlyStopCallback(Callback):
         else:
             raise exception  # 抛出陌生Error
 
+
 class FitlogCallback(Callback):
     """
     别名: :class:`fastNLP.FitlogCallback` :class:`fastNLP.core.callback.FitlogCallback`
@@ -463,7 +468,7 @@ class FitlogCallback(Callback):
                 assert 'test' not in data, "Cannot use `test` as DataSet key, when tester is passed."
             setattr(tester, 'verbose', 0)
             self.testers['test'] = tester
-
+        
         if isinstance(data, dict):
             for key, value in data.items():
                 assert isinstance(value, DataSet), f"Only DataSet object is allowed, not {type(value)}."
@@ -473,22 +478,22 @@ class FitlogCallback(Callback):
             self.datasets['test'] = data
         else:
             raise TypeError("data receives dict[DataSet] or DataSet object.")
-
+        
         self.verbose = verbose
         self._log_loss_every = log_loss_every
         self._avg_loss = 0
 
     def on_train_begin(self):
-        if (len(self.datasets)>0 or len(self.testers)>0 ) and self.trainer.dev_data is None:
+        if (len(self.datasets) > 0 or len(self.testers) > 0) and self.trainer.dev_data is None:
             raise RuntimeError("Trainer has no dev data, you cannot pass extra data to do evaluation.")
-
-        if len(self.datasets)>0:
+        
+        if len(self.datasets) > 0:
             for key, data in self.datasets.items():
                 tester = Tester(data=data, model=self.model, batch_size=self.batch_size, metrics=self.trainer.metrics,
                                 verbose=0)
                 self.testers[key] = tester
         fitlog.add_progress(total_steps=self.n_steps)
-
+    
     def on_backward_begin(self, loss):
         if self._log_loss_every>0:
             self._avg_loss += loss.item()
@@ -503,11 +508,11 @@ class FitlogCallback(Callback):
             eval_result['epoch'] = self.epoch
             fitlog.add_best_metric(eval_result)
         fitlog.add_metric(eval_result, step=self.step, epoch=self.epoch)
-        if len(self.testers)>0:
+        if len(self.testers) > 0:
             for key, tester in self.testers.items():
                 try:
                     eval_result = tester.test()
-                    if self.verbose!=0:
+                    if self.verbose != 0:
                         self.pbar.write("Evaluation on DataSet {}:".format(key))
                         self.pbar.write(tester._format_eval_results(eval_result))
                     fitlog.add_metric(eval_result, name=key, step=self.step, epoch=self.epoch)
@@ -515,10 +520,10 @@ class FitlogCallback(Callback):
                         fitlog.add_best_metric(eval_result, name=key)
                 except Exception:
                     self.pbar.write("Exception happens when evaluate on DataSet named `{}`.".format(key))
-
+    
     def on_train_end(self):
         fitlog.finish()
-
+    
     def on_exception(self, exception):
         fitlog.finish(status=1)
         if self._log_exception:

fastNLP/core/trainer.py

@@ -507,7 +507,8 @@ class Trainer(object):
 
                     seconds: float, 表示训练时长
                     以下三个内容只有在提供了dev_data的情况下会有。
-                    best_eval: Dict of Dict, 表示evaluation的结果。第一层的key为Metric的名称，第二层的key为具体的Metric
+                    best_eval: Dict of Dict, 表示evaluation的结果。第一层的key为Metric的名称，
+                                第二层的key为具体的Metric
                     best_epoch: int，在第几个epoch取得的最佳值
                     best_step: int, 在第几个step(batch)更新取得的最佳值