模型分析API文档#

flops#

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

获得指定网络的每秒浮点运算次数(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。

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：

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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：

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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。