Fix code in some docs (#763)

* Fix code in docs

* Fix code in docs

docs/en/quick_start/distillation_tutorial_en.md

@@ -13,10 +13,11 @@ by a demo of MobileNetV1 model on MNIST dataset. This tutorial following workflo
 
 PaddleSlim dependents on Paddle1.7. Please ensure that you have installed paddle correctly. Import Paddle and PaddleSlim as below:
 
-```
+```python
 import paddle
 import paddle.fluid as fluid
 import paddleslim as slim
+paddle.enable_static()
 ```
 
 ## 2. Define student_program and teacher_program

docs/en/quick_start/nas_tutorial_en.md

@@ -30,6 +30,8 @@ import numpy as np
 ```
 
 ## 2. initial SANAS instance
+
+Please set a unused port when build instance of SANAS.
 ```python
 sanas = slim.nas.SANAS(configs=[('MobileNetV2Space')], server_addr=("", 8337), save_checkpoint=None)
 ```

docs/en/tutorials/image_classification_sensitivity_analysis_tutorial_en.md

@@ -21,6 +21,7 @@ PaddleSlim dependents on Paddle1.7. Please ensure that you have installed paddle
 import paddle
 import paddle.fluid as fluid
 import paddleslim as slim
+paddle.enable_static()
 ```
 
 ## 2. Build model
@@ -62,7 +63,7 @@ def test(program):
     acc_top1_ns = []
     acc_top5_ns = []
     for data in test_reader():
-        acc_top1_n, acc_top5_n, _ = exe.run(
+        acc_top1_n, acc_top5_n, _ , _= exe.run(
             program,
             feed=data_feeder.feed(data),
             fetch_list=outputs)
@@ -82,7 +83,7 @@ Training model as below:
 
 ```python
 for data in train_reader():
-    acc1, acc5, loss = exe.run(train_program, feed=data_feeder.feed(data), fetch_list=outputs)
+    acc1, acc5, loss, _ = exe.run(train_program, feed=data_feeder.feed(data), fetch_list=outputs)
 print(np.mean(acc1), np.mean(acc5), np.mean(loss))
 ```
 
@@ -206,19 +207,22 @@ ratios = slim.prune.get_ratios_by_loss(s_0, loss)
 print(ratios)
 ```
 
-### 8.2 Pruning training network
+### 8.2 Pruning test network
+
+Note：The `only_graph` should be set to True while pruning test network. [Pruner API](https://paddlepaddle.github.io/PaddleSlim/api/prune_api/#pruner)
 
 
 ```python
 pruner = slim.prune.Pruner()
-print("FLOPs before pruning: {}".format(slim.analysis.flops(train_program)))
-pruned_program, _, _ = pruner.prune(
-        train_program,
+print("FLOPs before pruning: {}".format(slim.analysis.flops(val_program)))
+pruned_val_program, _, _ = pruner.prune(
+        val_program,
         fluid.global_scope(),
         params=ratios.keys(),
         ratios=ratios.values(),
-        place=place)
-print("FLOPs after pruning: {}".format(slim.analysis.flops(pruned_program)))
+        place=place,
+        only_graph=True)
+print("FLOPs after pruning: {}".format(slim.analysis.flops(pruned_val_program)))
 ```
 
 ### 8.3 Pruning test network
@@ -228,15 +232,14 @@ Note：The `only_graph` should be set to True while pruning test network. [Prune
 
 ```python
 pruner = slim.prune.Pruner()
-print("FLOPs before pruning: {}".format(slim.analysis.flops(val_program)))
-pruned_val_program, _, _ = pruner.prune(
-        val_program,
+print("FLOPs before pruning: {}".format(slim.analysis.flops(train_program)))
+pruned_program, _, _ = pruner.prune(
+        train_program,
         fluid.global_scope(),
         params=ratios.keys(),
         ratios=ratios.values(),
-        place=place,
-        only_graph=True)
-print("FLOPs after pruning: {}".format(slim.analysis.flops(pruned_val_program)))
+        place=place)
+print("FLOPs after pruning: {}".format(slim.analysis.flops(pruned_program)))
 ```
 
 Get accuracy of pruned model on test dataset:
@@ -252,7 +255,7 @@ Training pruned model:
 
 ```python
 for data in train_reader():
-    acc1, acc5, loss = exe.run(pruned_program, feed=data_feeder.feed(data), fetch_list=outputs)
+    acc1, acc5, loss, _ = exe.run(pruned_program, feed=data_feeder.feed(data), fetch_list=outputs)
 print(np.mean(acc1), np.mean(acc5), np.mean(loss))
 ```
 

docs/zh_cn/api_cn/dygraph/pruners/fpgm_filter_pruner.rst

@@ -21,8 +21,10 @@ FPGMFilterPruner
 
 .. code-block:: python
 
+   from paddle.vision.models import mobilenet_v1
    from paddleslim import FPGMFilterPruner
-   pruner = FPGMFilterPruner()       
+   net = mobilenet_v1(pretrained=False) 
+   pruner = FPGMFilterPruner(net, [1, 3, 224, 224])
 ..
  
    .. py:method:: prune_var(var_name, pruned_dims, pruned_ratio, apply="impretive")
@@ -49,11 +51,12 @@ FPGMFilterPruner
 
    .. code-block:: python
 
+      import paddle
       from paddle.vision.models import mobilenet_v1
       from paddleslim import FPGMFilterPruner
       net = mobilenet_v1(pretrained=False) 
       pruner = FPGMFilterPruner(net, [1, 3, 224, 224])
-      plan = pruner.prun_var("conv2d_26.w_0", [0])
+      plan = pruner.prune_var("conv2d_26.w_0", [0], pruned_ratio=0.5)
       print(f"plan: {plan}")
       paddle.summary(net, (1, 3, 224, 224))
    
@@ -81,11 +84,12 @@ FPGMFilterPruner
 
    .. code-block:: python
 
+      import paddle
       from paddle.vision.models import mobilenet_v1
       from paddleslim import FPGMFilterPruner
       net = mobilenet_v1(pretrained=False) 
       pruner = FPGMFilterPruner(net, [1, 3, 224, 224])
-      plan = pruner.prun_vars({"conv2d_26.w_0": 0.5}, [0])
+      plan = pruner.prune_vars({"conv2d_26.w_0": 0.5}, [0])
       print(f"plan: {plan}")
       paddle.summary(net, (1, 3, 224, 224))
 
@@ -129,6 +133,7 @@ FPGMFilterPruner
 
    .. code-block:: python
 
+      import paddle
       from paddle.vision.models import mobilenet_v1
       from paddleslim import FPGMFilterPruner
       import paddle.vision.transforms as T
@@ -189,6 +194,7 @@ FPGMFilterPruner
 
    .. code-block:: python
 
+      import paddle
       from paddle.vision.models import mobilenet_v1
       from paddleslim import FPGMFilterPruner
       import paddle.vision.transforms as T

docs/zh_cn/api_cn/dygraph/pruners/l1norm_filter_pruner.rst

@@ -20,9 +20,10 @@ L1NormFilterPruner
 **示例代码：**
 
 .. code-block:: python
-
-   from paddleslim import L1NormFilterPruner
-   pruner = L1NormFilterPruner()       
+    from paddle.vision.models import mobilenet_v1
+    from paddleslim import L1NormFilterPruner
+    net = mobilenet_v1(pretrained=False) 
+    pruner = L1NormFilterPruner(net, [1, 3, 224, 224])
 ..
  
    .. py:method:: prune_var(var_name, pruned_dims, pruned_ratio, apply="impretive")
@@ -48,7 +49,7 @@ L1NormFilterPruner
    点击 `AIStudio <>`_ 执行以下示例代码。
 
    .. code-block:: python
-
+      import paddle
       from paddle.vision.models import mobilenet_v1
       from paddleslim import L1NormFilterPruner
       net = mobilenet_v1(pretrained=False) 
@@ -80,7 +81,7 @@ L1NormFilterPruner
    点击 `AIStudio <>`_ 执行以下示例代码。
 
    .. code-block:: python
-
+      import paddle
       from paddle.vision.models import mobilenet_v1
       from paddleslim import L1NormFilterPruner
       net = mobilenet_v1(pretrained=False) 
@@ -128,7 +129,7 @@ L1NormFilterPruner
    点击 `AIStudio <>`_ 执行以下示例代码。
 
    .. code-block:: python
-
+      import paddle
       from paddle.vision.models import mobilenet_v1
       from paddleslim import L1NormFilterPruner
       import paddle.vision.transforms as T
@@ -188,7 +189,7 @@ L1NormFilterPruner
    点击 `AIStudio <>`_ 执行以下示例代码。
 
    .. code-block:: python
-
+      import paddle
       from paddle.vision.models import mobilenet_v1
       from paddleslim import L1NormFilterPruner
       import paddle.vision.transforms as T

docs/zh_cn/api_cn/dygraph/pruners/l2norm_filter_pruner.rst

@@ -20,9 +20,10 @@ L2NormFilterPruner
 **示例代码：**
 
 .. code-block:: python
-
+   from paddle.vision.models import mobilenet_v1
    from paddleslim import L2NormFilterPruner
-   pruner = L2NormFilterPruner()       
+   net = mobilenet_v1(pretrained=False) 
+   pruner = L2NormFilterPruner(net, [1, 3, 224, 224])
 ..
  
    .. py:method:: prune_var(var_name, pruned_dims, pruned_ratio, apply="impretive")
@@ -48,12 +49,12 @@ L2NormFilterPruner
    点击 `AIStudio <>`_ 执行以下示例代码。
 
    .. code-block:: python
-
+      import paddle
       from paddle.vision.models import mobilenet_v1
       from paddleslim import L2NormFilterPruner
       net = mobilenet_v1(pretrained=False) 
       pruner = L2NormFilterPruner(net, [1, 3, 224, 224])
-      plan = pruner.prun_var("conv2d_26.w_0", [0])
+      plan = pruner.prune_var("conv2d_26.w_0", [0], pruned_ratio=0.5)
       print(f"plan: {plan}")
       paddle.summary(net, (1, 3, 224, 224))
    
@@ -80,12 +81,12 @@ L2NormFilterPruner
    点击 `AIStudio <>`_ 执行以下示例代码。
 
    .. code-block:: python
-
+      import paddle
       from paddle.vision.models import mobilenet_v1
       from paddleslim import L2NormFilterPruner
       net = mobilenet_v1(pretrained=False) 
       pruner = L2NormFilterPruner(net, [1, 3, 224, 224])
-      plan = pruner.prun_vars({"conv2d_26.w_0": 0.5}, [0])
+      plan = pruner.prune_vars({"conv2d_26.w_0": 0.5}, [0])
       print(f"plan: {plan}")
       paddle.summary(net, (1, 3, 224, 224))
 
@@ -128,7 +129,7 @@ L2NormFilterPruner
    点击 `AIStudio <>`_ 执行以下示例代码。
 
    .. code-block:: python
-
+      import paddle
       from paddle.vision.models import mobilenet_v1
       from paddleslim import L2NormFilterPruner
       import paddle.vision.transforms as T
@@ -188,7 +189,7 @@ L2NormFilterPruner
    点击 `AIStudio <>`_ 执行以下示例代码。
 
    .. code-block:: python
-
+      import paddle
       from paddle.vision.models import mobilenet_v1
       from paddleslim import L2NormFilterPruner
       import paddle.vision.transforms as T

docs/zh_cn/api_cn/static/common/analysis_api.rst

@@ -27,11 +27,11 @@ FLOPs
 **示例：**
 
 .. code-block:: python
-
+    import paddle 
     import paddle.fluid as fluid
     from paddle.fluid.param_attr import ParamAttr
     from paddleslim.analysis import flops
-    
+    paddle.enable_static()
     def conv_bn_layer(input,
                       num_filters,
                       filter_size,
@@ -103,10 +103,11 @@ model_size
 
 .. code-block:: python
 
+    import paddle
     import paddle.fluid as fluid
     from paddle.fluid.param_attr import ParamAttr
     from paddleslim.analysis import model_size
-    
+    paddle.enable_static() 
     def conv_layer(input,
                       num_filters,
                       filter_size,

docs/zh_cn/api_cn/static/dist/single_distiller_api.rst

@@ -26,6 +26,7 @@ merge
 **使用示例：**
 
 .. code-block:: python
+
    import paddle
    import paddle.fluid as fluid
    import paddleslim.dist as dist
@@ -121,7 +122,8 @@ l2_loss
 **使用示例：**
 
 .. code-block:: python
-   import paddle
+
+   import paddle 
    import paddle.fluid as fluid
    import paddleslim.dist as dist
    paddle.enable_static()

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

@@ -49,7 +49,7 @@ 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=（"", 8881))
+   sanas = SANAS(configs=config, , server_addr=("",8821))
 
 .. note::
 
@@ -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=（"", 8882))
+      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:
@@ -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=（"", 8884))
+      sanas = SANAS(configs=config, server_addr=("",8823))
       input = paddle.static.data(name='input', shape=[None, 3, 32, 32], dtype='float32')
       tokens = ([0] * 25)
       archs = sanas.tokens2arch(tokens)[0]
@@ -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=（"", 8886))
+   rlnas = RLNAS(key='lstm', configs=config, server_addr=("",8824))
 
 
 .. 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=（"", 8887))
+  rlnas = RLNAS(key='lstm', configs=config, server_addr=("",8825))
   input = paddle.static.data(name='input', shape=[None, 3, 32, 32], dtype='float32')
   archs = rlnas.next_archs(1)[0]
   for arch in archs:
@@ -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=（"", 8889))
+  rlnas = RLNAS(key='lstm', configs=config, server_addr=("",8826))
   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=（"", 8891))
+  rlnas = RLNAS(key='lstm', configs=config, server_addr=("",8827))
   input = paddle.static.data(name='input', shape=[None, 3, 32, 32], dtype='float32')
   tokens = ([0] * 25)
   archs = rlnas.tokens2arch(tokens)[0]

docs/zh_cn/api_cn/static/prune/prune_api.rst

@@ -38,7 +38,15 @@ Pruner
    - **params(list<str>)** - 需要被裁剪的卷积层的参数的名称列表。可以通过以下方式查看模型中所有参数的名称:
    
    .. code-block:: python
-   
+
+      import paddle
+      paddle.enable_static()
+      program = paddle.static.Program()
+      with paddle.static.program_guard(main_program=program):
+          net = paddle.vision.models.mobilenet_v1()
+          data = paddle.static.data(name="data", shape=[1,3,32,32])
+          net(data)
+
       for block in program.blocks:
           for param in block.all_parameters():
               print("param: {}; shape: {}".format(param.name, param.shape))
@@ -68,11 +76,11 @@ Pruner
    点击 `AIStudio <https://aistudio.baidu.com/aistudio/projectDetail/200786>`_ 执行以下示例代码。
 
    .. code-block:: python
-   
+      import paddle   
       import paddle.fluid as fluid
       from paddle.fluid.param_attr import ParamAttr
       from paddleslim.prune import Pruner
-      
+      paddle.enable_static()
       def conv_bn_layer(input,
                         num_filters,
                         filter_size,
@@ -209,7 +217,7 @@ sensitivity
    from paddle.fluid.param_attr import ParamAttr
    from paddleslim.prune import sensitivity
    import paddle.dataset.mnist as reader
-   
+   paddle.enable_static()   
    def conv_bn_layer(input,
                      num_filters,
                      filter_size,

docs/zh_cn/quick_start/static/distillation_tutorial.md

@@ -15,7 +15,7 @@
 
 PaddleSlim依赖Paddle2.0版本，请确认已正确安装Paddle，然后按以下方式导入Paddle和PaddleSlim:
 
-```
+```python
 import paddle
 import numpy as np
 import paddleslim as slim

docs/zh_cn/tutorials/pruning/dygraph/filter_pruning.md

@@ -7,6 +7,7 @@
 PaddlePaddle提供的`vision`模块提供了一些构建好的分类模型结构，并提供在`ImageNet`数据集上的预训练模型。为了简化教程，我们不再重新定义网络结构，而是直接从`vision`模块导入模型结构。代码如下所示，我们导入`MobileNetV1`模型，并查看模型的结构信息。
 
 ```python
+from __future__ import print_function
 import paddle
 from paddle.vision.models import mobilenet_v1
 net = mobilenet_v1(pretrained=False)
@@ -30,7 +31,6 @@ val_dataset = paddle.vision.datasets.Cifar10(mode="test", backend="cv2",transfor
 我们可以通过以下代码查看训练集和测试集的样本数量，并尝试取出训练集中的第一个样本，观察其图片的`shape`和对应的`label`。
 
 ```python
-from __future__ import print_function
 print(f'train samples count: {len(train_dataset)}')
 print(f'val samples count: {len(val_dataset)}')
 for data in train_dataset:

docs/zh_cn/tutorials/pruning/dygraph/self_defined_filter_pruning.md

@@ -22,7 +22,7 @@ def cal_mask(self, var_name, pruned_ratio, group):
 
 如图1-1所示，在给定模型中有两个卷积层，第一个卷积层有3个`filters`，第二个卷积层有2个`filters`。如果删除第一个卷积绿色的`filter`，第一个卷积的输出特征图的通道数也会减1，同时需要删掉第二个卷积层绿色的`kernels`。如上所述的两个卷积共同组成一个group，表示如下：
 
-```python
+```
 group = {
             "conv_1.weight":{
                 "pruned_dims": [0],
@@ -94,7 +94,7 @@ class L2NormFilterPruner(FilterPruner):
 
 如上述代码所示，我们重载了`FilterPruner`基类的`cal_mask`方法，并在`L1NormFilterPruner`代码基础上，修改了计算通道重要性的语句，将其修改为了计算L2Norm的逻辑：
 
-```python
+```
 scores = np.sqrt(np.sum(np.square(value), axis=tuple(reduce_dims)))
 ```
 

docs/zh_cn/tutorials/pruning/static/image_classification_sensitivity_analysis_tutorial.md

@@ -83,7 +83,7 @@ def test(program):
 
 ```python
 for data in train_reader():
-    acc1, acc5, loss = exe.run(train_program, feed=data_feeder.feed(data), fetch_list=outputs)
+    acc1, acc5, loss, _ = exe.run(train_program, feed=data_feeder.feed(data), fetch_list=outputs)
 print(np.mean(acc1), np.mean(acc5), np.mean(loss))
 ```
 
@@ -213,24 +213,9 @@ ratios = slim.prune.get_ratios_by_loss(s_0, loss)
 print(ratios)
 ```
 
-### 8.2 剪裁训练网络
+### 8.2 剪裁测试网络
 
-
-```python
-pruner = slim.prune.Pruner()
-print("FLOPs before pruning: {}".format(slim.analysis.flops(train_program)))
-pruned_program, _, _ = pruner.prune(
-        train_program,
-        fluid.global_scope(),
-        params=ratios.keys(),
-        ratios=ratios.values(),
-        place=place)
-print("FLOPs after pruning: {}".format(slim.analysis.flops(pruned_program)))
-```
-
-### 8.3 剪裁测试网络
-
->注意：对测试网络进行剪裁时，需要将`only_graph`设置为True，具体原因请参考[Pruner API文档](https://paddleslim.readthedocs.io/zh_CN/latest/api_cn/static/prune/prune_api.html#paddleslim.prune.Pruner)
+>注意：剪裁测试网络要在剪裁训练网络之前。对测试网络进行剪裁时，需要将`only_graph`设置为True，具体原因请参考[Pruner API文档](https://paddleslim.readthedocs.io/zh_CN/latest/api_cn/static/prune/prune_api.html#paddleslim.prune.Pruner)
 
 
 ```python
@@ -252,6 +237,21 @@ print("FLOPs after pruning: {}".format(slim.analysis.flops(pruned_val_program)))
 test(pruned_val_program)
 ```
 
+### 8.3 剪裁训练网络
+
+```python
+pruner = slim.prune.Pruner()
+print("FLOPs before pruning: {}".format(slim.analysis.flops(train_program)))
+pruned_program, _, _ = pruner.prune(
+        train_program,
+        fluid.global_scope(),
+        params=ratios.keys(),
+        ratios=ratios.values(),
+        place=place)
+print("FLOPs after pruning: {}".format(slim.analysis.flops(pruned_program)))
+```
+
+
 ### 8.4 训练剪裁后的模型
 
 对剪裁后的模型在训练集上训练一个`epoch`:

docs/zh_cn/tutorials/quant/static/embedding_quant_tutorial.md

@@ -8,10 +8,34 @@ Embedding量化仅能减少模型参数的体积，并不能显著提升模型
 在预测时调用paddleslim `quant_embedding`接口，主要实现代码如下：
 
 ```python
-import paddleslim
-place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
-exe = paddle.static.Executor(place)
-main_program = paddleslim.quant.quant_embedding(main_program, place, config)
+import paddle
+import paddle.fluid as fluid
+import paddleslim.quant as quant
+paddle.enable_static()
+train_program = fluid.Program()
+with fluid.program_guard(train_program):
+    input_word = fluid.data(name="input_word", shape=[None, 1], dtype='int64')
+    input_emb = fluid.embedding(
+        input=input_word,
+        is_sparse=False,
+        size=[100, 128],
+        param_attr=fluid.ParamAttr(name='emb',
+        initializer=fluid.initializer.Uniform(-0.005, 0.005)))
+
+infer_program = train_program.clone(for_test=True)
+
+use_gpu = True
+place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
+exe = fluid.Executor(place)
+exe.run(fluid.default_startup_program())
+
+config = {
+         'quantize_op_types': ['lookup_table'],
+         'lookup_table': {
+             'quantize_type': 'abs_max'
+             }
+         }
+quant_program = quant.quant_embedding(infer_program, place, config)
 ```
 
 详细代码与例程请参考：[Embedding量化](https://github.com/PaddlePaddle/PaddleSlim/tree/develop/demo/quant/quant_embedding)