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+ 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：
+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：
+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。 +