deeplearning模型分析
FLOPs
paddleslim.analysis.flops(program, detail=False)
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：
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)
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：
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
classpaddleslim.analysis.TableLatencyEvaluator(table_file, delimiter=", ")
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) - 在硬件延时评估表中，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。
latency(graph)
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。