## Pruner

paddleslim.prune.Pruner(criterion="l1_norm")源代码

  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：

    from paddleslim.prune import Pruner
    pruner = Pruner()

paddleslim.prune.Pruner.prune(program, scope, params, ratios, place=None, lazy=False, only_graph=False, param_backup=False, param_shape_backup=False)源代码

  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。

- 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) - 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:

      for block in program.blocks:
          for param in block.all_parameters():
              print("param: {}; shape: {}".format(param.name, param.shape))

- ratios(list) - 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执行以下示例代码。

    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

paddleslim.prune.sensitivity(program, place, param_names, eval_func, sensitivities_file=None, pruned_ratios=None) 源代码

  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。

- 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) - 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:

    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) - 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：

    {"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运行以下示例代码。

    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

paddleslim.prune.merge_sensitive(sensitivities)源代码

  合并多个敏感度信息。

参数：

- sensitivities(list | list) - 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：

    {"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：

    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

paddleslim.prune.load_sensitivities(sensitivities_file)源代码

  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：

    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, 'w') as f:
        pickle.dump(sen, f)
    sensitivities = load_sensitivities(sensitivities_file)
    print(sensitivities)

## get_ratios_by_loss

paddleslim.prune.get_ratios_by_loss(sensitivities, loss)源代码

  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：

    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)

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