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)