fix_docs_nas

docs/en/quick_start/nas_tutorial_en.md

@@ -33,7 +33,7 @@ import numpy as np
 
 Please set a unused port when build instance of SANAS.
 ```python
-sanas = slim.nas.SANAS(configs=[('MobileNetV2Space')], server_addr=("", 8337), save_checkpoint=None)
+sanas = slim.nas.SANAS(configs=[('MobileNetV2Space')], server_addr=("", 8911), save_checkpoint=None)
 ```
 
 ## 3. define function about building program

docs/zh_cn/api_cn/dygraph/ofa/convert_supernet_api.rst

@@ -85,8 +85,8 @@ PaddleSlim提供了三种方式构造超网络，下面分别介绍这三种方
       models += [nn.Conv2D(4, 4, 3, groups=4)]
       self.models = paddle.nn.Sequential(*models)
 
-     def forward(self, inputs):
-       return self.models(inputs)
+    def forward(self, inputs):
+        return self.models(inputs)
 
 方式三
 ------------------

docs/zh_cn/api_cn/static/nas/nas_api.rst

@@ -88,7 +88,7 @@ SANAS（Simulated Annealing Neural Architecture Search）是基于模拟退火
       from paddleslim.nas import SANAS
       config = [('MobileNetV2Space')]
       paddle.enable_static()
-      sanas = SANAS(configs=config, , server_addr=("",8822))
+      sanas = SANAS(configs=config, server_addr=("",8822))
       input = paddle.static.data(name='input', shape=[None, 3, 32, 32], dtype='float32')
       archs = sanas.next_archs()
       for arch in archs:
@@ -115,7 +115,7 @@ SANAS（Simulated Annealing Neural Architecture Search）是基于模拟退火
       from paddleslim.nas import SANAS
       config = [('MobileNetV2Space')]
       paddle.enable_static()
-      sanas = SANAS(configs=config, server_addr=（"", 8883))
+      sanas = SANAS(configs=config, server_addr=("", 8823))
       archs = sanas.next_archs()
 
       ### 假设网络计算出来的score是1，实际代码中使用时需要返回真实score。
@@ -142,7 +142,7 @@ SANAS（Simulated Annealing Neural Architecture Search）是基于模拟退火
       from paddleslim.nas import SANAS
       config = [('MobileNetV2Space')]
       paddle.enable_static()
-      sanas = SANAS(configs=config, server_addr=("",8823))
+      sanas = SANAS(configs=config, server_addr=("", 8824))
       input = paddle.static.data(name='input', shape=[None, 3, 32, 32], dtype='float32')
       tokens = ([0] * 25)
       archs = sanas.tokens2arch(tokens)[0]
@@ -163,7 +163,7 @@ SANAS（Simulated Annealing Neural Architecture Search）是基于模拟退火
       from paddleslim.nas import SANAS
       config = [('MobileNetV2Space')]
       paddle.enable_static()
-      sanas = SANAS(configs=config, server_addr=（"", 8885))
+      sanas = SANAS(configs=config, server_addr=("", 8825))
       print(sanas.current_info())
 
 
@@ -233,7 +233,7 @@ RLNAS (Reinforcement Learning Neural Architecture Search）是基于强化学习
    config = [('MobileNetV2Space')]
 
    paddle.enable_static()
-   rlnas = RLNAS(key='lstm', configs=config, server_addr=("",8824))
+   rlnas = RLNAS(key='lstm', configs=config, server_addr=("",8826))
 
 
 .. py:method:: next_archs(obs=None)
@@ -255,7 +255,7 @@ RLNAS (Reinforcement Learning Neural Architecture Search）是基于强化学习
   from paddleslim.nas import RLNAS
   config = [('MobileNetV2Space')]
   paddle.enable_static()
-  rlnas = RLNAS(key='lstm', configs=config, server_addr=("",8825))
+  rlnas = RLNAS(key='lstm', configs=config, server_addr=("",8827))
   input = paddle.static.data(name='input', shape=[None, 3, 32, 32], dtype='float32')
   archs = rlnas.next_archs(1)[0]
   for arch in archs:
@@ -280,7 +280,7 @@ RLNAS (Reinforcement Learning Neural Architecture Search）是基于强化学习
   from paddleslim.nas import RLNAS
   config = [('MobileNetV2Space')]
   paddle.enable_static()
-  rlnas = RLNAS(key='lstm', configs=config, server_addr=（"", 8888))
+  rlnas = RLNAS(key='lstm', configs=config, server_addr=("", 8828))
   rlnas.next_archs(1)
   rlnas.reward(1.0)
 
@@ -307,7 +307,7 @@ RLNAS (Reinforcement Learning Neural Architecture Search）是基于强化学习
   from paddleslim.nas import RLNAS
   config = [('MobileNetV2Space')]
   paddle.enable_static()
-  rlnas = RLNAS(key='lstm', configs=config, server_addr=("",8826))
+  rlnas = RLNAS(key='lstm', configs=config, server_addr=("",8829))
   archs = rlnas.final_archs(1)
   print(archs)
 
@@ -330,7 +330,7 @@ RLNAS (Reinforcement Learning Neural Architecture Search）是基于强化学习
   from paddleslim.nas import RLNAS
   config = [('MobileNetV2Space')]
   paddle.enable_static()
-  rlnas = RLNAS(key='lstm', configs=config, server_addr=("",8827))
+  rlnas = RLNAS(key='lstm', configs=config, server_addr=("",8830))
   input = paddle.static.data(name='input', shape=[None, 3, 32, 32], dtype='float32')
   tokens = ([0] * 25)
   archs = rlnas.tokens2arch(tokens)[0]

docs/zh_cn/quick_start/static/nas_tutorial.md

@@ -160,8 +160,8 @@ sanas.reward(float(finally_reward[1]))
 ```python
 for step in range(3):
     archs = sanas.next_archs()[0]
-    exe, train_program, eval_program, inputs, avg_cost, acc_top1, acc_top5 = build_program(archs)
-    train_loader, eval_loader = input_data(inputs)
+    exe, train_program, eval_program, (images,label), avg_cost, acc_top1, acc_top5 = build_program(archs)
+    train_loader, eval_loader = input_data(images, label)
 
     current_flops = slim.analysis.flops(train_program)
     if current_flops > 321208544:

docs/zh_cn/tutorials/nas/dygraph/nas_ofa.md

@@ -16,13 +16,13 @@ OFA的基本流程分为以下步骤：
    PaddleSlim提供了三种获得超网络的方式，具体可以参考[超网络转换](https://paddleslim.readthedocs.io/zh_CN/latest/api_cn/dygraph/ofa/convert_supernet_api.html)。
 
 ```python
-  import paddle
-  from paddle.vision.models import mobilenet_v1
-  from paddleslim.nas.ofa.convert_super import Convert, supernet
+import paddle
+from paddle.vision.models import mobilenet_v1
+from paddleslim.nas.ofa.convert_super import Convert, supernet
 
-  model = mobilenet_v1()
-  sp_net_config = supernet(kernel_size=(3, 5, 7), expand_ratio=[1, 2, 4])
-  sp_model = Convert(sp_net_config).convert(model)
+model = mobilenet_v1()
+sp_net_config = supernet(kernel_size=(3, 5, 7), expand_ratio=[1, 2, 4])
+sp_model = Convert(sp_net_config).convert(model)
 ```
 
 ### 2. 训练配置

docs/zh_cn/tutorials/nas/static/sanas_darts_space.md

@@ -262,14 +262,14 @@ sa_nas.reward(float(valid_top1_list[-1] + valid_top1_list[-2]) / 2)
 ### 10. 利用demo下的脚本启动搜索
 
 搜索文件位于: [darts_sanas_demo](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/demo/nas/sanas_darts_space.py)，搜索过程中限制模型参数量为不大于3.77M。
-```python
+```shell
 cd demo/nas/
 python darts_nas.py
 ```
 
 ### 11. 利用demo下的脚本启动最终实验
 最终实验文件位于: [darts_sanas_demo](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/demo/nas/sanas_darts_space.py)，最终实验需要训练600epoch。以下示例输入token为`[5, 5, 0, 5, 5, 10, 7, 7, 5, 7, 7, 11, 10, 12, 10, 0, 5, 3, 10, 8]`。
-```python
+```shell
 cd demo/nas/
 python darts_nas.py --token 5 5 0 5 5 10 7 7 5 7 7 11 10 12 10 0 5 3 10 8 --retain_epoch 600
 ```