FLOPs#

paddleslim.analysis.flops(program, detail=False) [源代码]

获得指定网络的浮点运算次数(FLOPs)。

参数：

  • program(paddle.fluid.Program) - 待分析的目标网络。更多关于Program的介绍请参考：Program概念介绍。

  • detail(bool) - 是否返回每个卷积层的FLOPs。默认为False。

  • only_conv(bool) - 如果设置为True，则仅计算卷积层和全连接层的FLOPs，即浮点数的乘加（multiplication-adds）操作次数。如果设置为False，则也会计算卷积和全连接层之外的操作的FLOPs。

返回值：

  • flops(float) - 整个网络的FLOPs。

  • params2flops(dict) - 每层卷积对应的FLOPs，其中key为卷积层参数名称，value为FLOPs值。

示例：

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
from paddleslim.analysis import flops

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")

print("FLOPs: {}".format(flops(main_program)))

model_size#

paddleslim.analysis.model_size(program) [源代码]

获得指定网络的参数数量。

参数：

  • program(paddle.fluid.Program) - 待分析的目标网络。更多关于Program的介绍请参考：Program概念介绍。

返回值：

  • model_size(int) - 整个网络的参数数量。

示例：

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
from paddleslim.analysis import model_size

def conv_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")
    return conv

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_layer(input, 8, 3, "conv1")
    conv2 = conv_layer(conv1, 8, 3, "conv2")
    sum1 = conv1 + conv2
    conv3 = conv_layer(sum1, 8, 3, "conv3")
    conv4 = conv_layer(conv3, 8, 3, "conv4")
    sum2 = conv4 + sum1
    conv5 = conv_layer(sum2, 8, 3, "conv5")
    conv6 = conv_layer(conv5, 8, 3, "conv6")

print("FLOPs: {}".format(model_size(main_program)))

TableLatencyEvaluator#

paddleslim.analysis.TableLatencyEvaluator(table_file, delimiter=",") [源代码]

基于硬件延时表的模型延时评估器。

参数：

  • table_file(str) - 所使用的延时评估表的绝对路径。关于演示评估表格式请参考：PaddleSlim硬件延时评估表格式

  • delimiter(str) - 硬件延时评估表中，操作信息之前所使用的分割符，默认为英文字符逗号。

返回值：

  • Evaluator - 硬件延时评估器的实例。
paddleslim.analysis.TableLatencyEvaluator.latency(graph) [源代码]

获得指定网络的预估延时。

参数：

  • graph(Program) - 待预估的目标网络。

返回值：

  • latency - 目标网络的预估延时。