卷积层通道剪裁
================

Pruner
----------

.. py:class:: paddleslim.prune.Pruner(criterion="l1_norm")

`源代码 <https://github.com/PaddlePaddle/PaddleSlim/blob/develop/paddleslim/prune/pruner.py#L28>`_

对卷积网络的通道进行一次剪裁。剪裁一个卷积层的通道，是指剪裁该卷积层输出的通道。卷积层的权重形状为 ``[output_channel, input_channel, kernel_size, kernel_size]`` ，通过剪裁该权重的第一纬度达到剪裁输出通道数的目的。

**参数：**

- **criterion** - 评估一个卷积层内通道重要性所参考的指标。目前仅支持 ``l1_norm`` 。默认为 ``l1_norm`` 。

**返回：** 一个Pruner类的实例

**示例代码：**

.. code-block:: python

   from paddleslim.prune import Pruner
   pruner = Pruner()

..
 
   .. py:method:: paddleslim.prune.Pruner.prune(program, scope, params, ratios, place=None, lazy=False, only_graph=False, param_backup=False, param_shape_backup=False)

   对目标网络的一组卷积层的权重进行裁剪。
   
   **参数：**
   
   - **program(paddle.fluid.Program)** - 要裁剪的目标网络。更多关于Program的介绍请参考：`Program概念介绍 <https://www.paddlepaddle.org.cn/documentation/docs/zh/api_cn/fluid_cn/Program_cn.html#program>`_。
   
   - **scope(paddle.fluid.Scope)** - 要裁剪的权重所在的 ``scope`` ，Paddle中用 ``scope`` 实例存放模型参数和运行时变量的值。Scope中的参数值会被 ``inplace`` 的裁剪。更多介绍请参考: `Scope概念介绍 <>`_
   
   - **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
   
      for block in program.blocks:
          for param in block.all_parameters():
              print("param: {}; shape: {}".format(param.name, param.shape))
   
   - **ratios(list<float>)** - 用于裁剪 ``params`` 的剪切率，类型为列表。该列表长度必须与 ``params`` 的长度一致。
   
   - **place(paddle.fluid.Place)** - 待裁剪参数所在的设备位置，可以是 ``CUDAPlace`` 或 ``CPUPlace`` 。[Place概念介绍]()
   
   - **lazy(bool)** - ``lazy`` 为True时，通过将指定通道的参数置零达到裁剪的目的，参数的 ``shape保持不变`` ； ``lazy`` 为False时，直接将要裁的通道的参数删除，参数的 ``shape`` 会发生变化。
   
   - **only_graph(bool)** - 是否只裁剪网络结构。在Paddle中，Program定义了网络结构，Scope存储参数的数值。一个Scope实例可以被多个Program使用，比如定义了训练网络的Program和定义了测试网络的Program是使用同一个Scope实例的。 ``only_graph`` 为True时，只对Program中定义的卷积的通道进行剪裁； ``only_graph`` 为false时，Scope中卷积参数的数值也会被剪裁。默认为False。
   
   - **param_backup(bool)** - 是否返回对参数值的备份。默认为False。
   
   - **param_shape_backup(bool)** - 是否返回对参数 ``shape`` 的备份。默认为False。
   
   **返回：**
   
   - **pruned_program(paddle.fluid.Program)** - 被裁剪后的Program。
   
   - **param_backup(dict)** - 对参数数值的备份，用于恢复Scope中的参数数值。
   
   - **param_shape_backup(dict)** - 对参数形状的备份。
   
   **示例：**
   
   点击 `AIStudio <https://aistudio.baidu.com/aistudio/projectDetail/200786>`_ 执行以下示例代码。

   .. code-block:: python
   
      import paddle.fluid as fluid
      from paddle.fluid.param_attr import ParamAttr
      from paddleslim.prune import Pruner
      
      def conv_bn_layer(input,
                        num_filters,
                        filter_size,
                        name,
                        stride=1,
                        groups=1,
                        act=None):
          conv = fluid.layers.conv2d(
              input=input,
              num_filters=num_filters,
              filter_size=filter_size,
              stride=stride,
              padding=(filter_size - 1) // 2,
              groups=groups,
              act=None,
              param_attr=ParamAttr(name=name + "_weights"),
              bias_attr=False,
              name=name + "_out")
          bn_name = name + "_bn"
          return fluid.layers.batch_norm(
              input=conv,
              act=act,
              name=bn_name + '_output',
              param_attr=ParamAttr(name=bn_name + '_scale'),
              bias_attr=ParamAttr(bn_name + '_offset'),
              moving_mean_name=bn_name + '_mean',
              moving_variance_name=bn_name + '_variance', )
      
      main_program = fluid.Program()
      startup_program = fluid.Program()
      #   X       X              O       X              O
      # conv1-->conv2-->sum1-->conv3-->conv4-->sum2-->conv5-->conv6
      #     |            ^ |                    ^
      #     |____________| |____________________|
      #
      # X: prune output channels
      # O: prune input channels
      with fluid.program_guard(main_program, startup_program):
          input = fluid.data(name="image", shape=[None, 3, 16, 16])
          conv1 = conv_bn_layer(input, 8, 3, "conv1")
          conv2 = conv_bn_layer(conv1, 8, 3, "conv2")
          sum1 = conv1 + conv2
          conv3 = conv_bn_layer(sum1, 8, 3, "conv3")
          conv4 = conv_bn_layer(conv3, 8, 3, "conv4")
          sum2 = conv4 + sum1
          conv5 = conv_bn_layer(sum2, 8, 3, "conv5")
          conv6 = conv_bn_layer(conv5, 8, 3, "conv6")
      
      place = fluid.CPUPlace()
      exe = fluid.Executor(place)
      scope = fluid.Scope()
      exe.run(startup_program, scope=scope)
      pruner = Pruner()
      main_program, _, _ = pruner.prune(
          main_program,
          scope,
          params=["conv4_weights"],
          ratios=[0.5],
          place=place,
          lazy=False,
          only_graph=False,
          param_backup=False,
          param_shape_backup=False)
      
      for param in main_program.global_block().all_parameters():
          if "weights" in param.name:
              print("param name: {}; param shape: {}".format(param.name, param.shape))
      

sensitivity
--------------

.. py:function:: paddleslim.prune.sensitivity(program, place, param_names, eval_func, sensitivities_file=None, pruned_ratios=None)

`源代码 <https://github.com/PaddlePaddle/PaddleSlim/blob/develop/paddleslim/prune/sensitive.py>`_

计算网络中每个卷积层的敏感度。每个卷积层的敏感度信息统计方法为：依次剪掉当前卷积层不同比例的输出通道数，在测试集上计算剪裁后的精度损失。得到敏感度信息后，可以通过观察或其它方式确定每层卷积的剪裁率。

**参数：**

- **program(paddle.fluid.Program)** - 待评估的目标网络。更多关于Program的介绍请参考：`Program概念介绍 <https://www.paddlepaddle.org.cn/documentation/docs/zh/api_cn/fluid_cn/Program_cn.html#program>`_。

- **place(paddle.fluid.Place)** - 待分析的参数所在的设备位置，可以是 ``CUDAPlace`` 或 ``CPUPlace`` 。[Place概念介绍]()

- **param_names(list<str>)** - 待分析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
   for block in program.blocks:
       for param in block.all_parameters():
           print("param: {}; shape: {}".format(param.name, param.shape))

- **eval_func(function)** - 用于评估裁剪后模型效果的回调函数。该回调函数接受被裁剪后的 ``program`` 为参数，返回一个表示当前program的精度，用以计算当前裁剪带来的精度损失。

- **sensitivities_file(str)** - 保存敏感度信息的本地文件系统的文件。在敏感度计算过程中，会持续将新计算出的敏感度信息追加到该文件中。重启任务后，文件中已有敏感度信息不会被重复计算。该文件可以用 ``pickle`` 加载。

- **pruned_ratios(list<float>)** - 计算卷积层敏感度信息时，依次剪掉的通道数比例。默认为 ``[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]`` 。

**返回：**

- **sensitivities(dict)** - 存放敏感度信息的dict，其格式为：

.. code-block:: python

  {"weight_0":
     {0.1: 0.22,
      0.2: 0.33
     },
   "weight_1":
     {0.1: 0.21,
      0.2: 0.4
     }
  }

其中， ``weight_0`` 是卷积层参数的名称， ``sensitivities['weight_0']`` 的 ``value`` 为剪裁比例， ``value`` 为精度损失的比例。

**示例：**

点击 `AIStudio <https://aistudio.baidu.com/aistudio/projectdetail/201401>`_ 运行以下示例代码。

.. code-block:: python

   import paddle
   import numpy as np
   import paddle.fluid as fluid
   from paddle.fluid.param_attr import ParamAttr
   from paddleslim.prune import sensitivity
   import paddle.dataset.mnist as reader
   
   def conv_bn_layer(input,
                     num_filters,
                     filter_size,
                     name,
                     stride=1,
                     groups=1,
                     act=None):
       conv = fluid.layers.conv2d(
           input=input,
           num_filters=num_filters,
           filter_size=filter_size,
           stride=stride,
           padding=(filter_size - 1) // 2,
           groups=groups,
           act=None,
           param_attr=ParamAttr(name=name + "_weights"),
           bias_attr=False,
           name=name + "_out")
       bn_name = name + "_bn"
       return fluid.layers.batch_norm(
           input=conv,
           act=act,
           name=bn_name + '_output',
           param_attr=ParamAttr(name=bn_name + '_scale'),
           bias_attr=ParamAttr(bn_name + '_offset'),
           moving_mean_name=bn_name + '_mean',
           moving_variance_name=bn_name + '_variance', )
   
   main_program = fluid.Program()
   startup_program = fluid.Program()
   #   X       X              O       X              O
   # conv1-->conv2-->sum1-->conv3-->conv4-->sum2-->conv5-->conv6
   #     |            ^ |                    ^
   #     |____________| |____________________|
   #
   # X: prune output channels
   # O: prune input channels
   image_shape = [1,28,28]
   with fluid.program_guard(main_program, startup_program):
       image = fluid.data(name='image', shape=[None]+image_shape, dtype='float32')
       label = fluid.data(name='label', shape=[None, 1], dtype='int64')  
       conv1 = conv_bn_layer(image, 8, 3, "conv1")
       conv2 = conv_bn_layer(conv1, 8, 3, "conv2")
       sum1 = conv1 + conv2
       conv3 = conv_bn_layer(sum1, 8, 3, "conv3")
       conv4 = conv_bn_layer(conv3, 8, 3, "conv4")
       sum2 = conv4 + sum1
       conv5 = conv_bn_layer(sum2, 8, 3, "conv5")
       conv6 = conv_bn_layer(conv5, 8, 3, "conv6")
       out = fluid.layers.fc(conv6, size=10, act="softmax")
   #    cost = fluid.layers.cross_entropy(input=out, label=label)
   #    avg_cost = fluid.layers.mean(x=cost)
       acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1)
   #    acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5)
   
   
   place = fluid.CPUPlace()
   exe = fluid.Executor(place)
   exe.run(startup_program)
   
   val_reader = paddle.batch(reader.test(), batch_size=128)
   val_feeder = feeder = fluid.DataFeeder(
           [image, label], place, program=main_program)
   
   def eval_func(program):
   
       acc_top1_ns = []
       for data in val_reader():
           acc_top1_n = exe.run(program,
                                feed=val_feeder.feed(data),
                                fetch_list=[acc_top1.name])
           acc_top1_ns.append(np.mean(acc_top1_n))
       return np.mean(acc_top1_ns)
   param_names = []
   for param in main_program.global_block().all_parameters():
       if "weights" in param.name:
           param_names.append(param.name)
   sensitivities = sensitivity(main_program,
                               place,
                               param_names,
                               eval_func,
                               sensitivities_file="./sensitive.data",
                               pruned_ratios=[0.1, 0.2, 0.3])
   print(sensitivities)
   

merge_sensitive
----------------

.. py:function:: paddleslim.prune.merge_sensitive(sensitivities)

`源代码 <https://github.com/PaddlePaddle/PaddleSlim/blob/develop/paddleslim/prune/sensitive.py>`_

合并多个敏感度信息。

参数：

- **sensitivities(list<dict> | list<str>)** - 待合并的敏感度信息，可以是字典的列表，或者是存放敏感度信息的文件的路径列表。

返回：

- **sensitivities(dict)** - 合并后的敏感度信息。其格式为：

.. code-block:: bash

   {"weight_0":
      {0.1: 0.22,
       0.2: 0.33
      },
    "weight_1":
      {0.1: 0.21,
       0.2: 0.4
      }
   }
   

其中， ``weight_0`` 是卷积层参数的名称， ``sensitivities['weight_0']`` 的 ``value`` 为剪裁比例， ``value`` 为精度损失的比例。

示例：

.. code-block:: python

   from paddleslim.prune import merge_sensitive
   sen0 = {"weight_0":
      {0.1: 0.22,
       0.2: 0.33
      },
    "weight_1":
      {0.1: 0.21,
       0.2: 0.4
      }
   }
   sen1 = {"weight_0":
      {0.3: 0.41,
      },
    "weight_2":
      {0.1: 0.10,
       0.2: 0.35
      }
   }
   sensitivities = merge_sensitive([sen0, sen1])
   print(sensitivities)


load_sensitivities
---------------------

.. py:function:: paddleslim.prune.load_sensitivities(sensitivities_file)

`源代码 <https://github.com/PaddlePaddle/PaddleSlim/blob/develop/paddleslim/prune/sensitive.py#L184>`_

从文件中加载敏感度信息。

参数：

- **sensitivities_file(str)** - 存放敏感度信息的本地文件.

返回：

- **sensitivities(dict)** - 敏感度信息。

示例：

.. code-block:: python

  import pickle
  from paddleslim.prune import load_sensitivities
  sen = {"weight_0":
     {0.1: 0.22,
      0.2: 0.33
     },
   "weight_1":
     {0.1: 0.21,
      0.2: 0.4
     }
  }
  sensitivities_file = "sensitive_api_demo.data"
  with open(sensitivities_file, 'wb') as f:
      pickle.dump(sen, f)
  sensitivities = load_sensitivities(sensitivities_file)
  print(sensitivities)

get_ratios_by_loss
-------------------

.. py:function:: paddleslim.prune.get_ratios_by_loss(sensitivities, loss)

`源代码 <https://github.com/PaddlePaddle/PaddleSlim/blob/develop/paddleslim/prune/sensitive.py>`_

根据敏感度和精度损失阈值计算出一组剪切率。对于参数 ``w`` , 其剪裁率为使精度损失低于 ``loss`` 的最大剪裁率。

**参数：**

- **sensitivities(dict)** - 敏感度信息。

- **loss** - 精度损失阈值。

**返回：**

- **ratios(dict)** - 一组剪切率。 ``key`` 是待剪裁参数的名称。 ``value`` 是对应参数的剪裁率。

**示例：**

.. code-block:: python
   
  from paddleslim.prune import get_ratios_by_loss
  sen = {"weight_0":
     {0.1: 0.22,
      0.2: 0.33
     },
   "weight_1":
     {0.1: 0.21,
      0.2: 0.4
     }
  }
  
  ratios = get_ratios_by_loss(sen, 0.3)
  print(ratios)
