模型分析
=======

FLOPs
-----

.. py:function:: paddleslim.analysis.flops(program, detail=False)

`源代码 <https://github.com/PaddlePaddle/PaddleSlim/blob/develop/paddleslim/analysis/flops.py>`_

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 <https://www.paddlepaddle.org.cn/documentation/docs/zh/api_cn/fluid_cn/Program_cn.html#program>`_。

- **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：**

.. code-block:: python
    import paddle 
    import paddle.fluid as fluid
    from paddle.fluid.param_attr import ParamAttr
    from paddleslim.analysis import flops
    paddle.enable_static()
    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
----------

.. py:function:: paddleslim.analysis.model_size(program)

`源代码 <https://github.com/PaddlePaddle/PaddleSlim/blob/develop/paddleslim/analysis/model_size.py>`_

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 <https://www.paddlepaddle.org.cn/documentation/docs/zh/api_cn/fluid_cn/Program_cn.html#program>`_。

**返回值：**

- **model_size(int)** - 整个网络的参数数量。

**示例：**

.. code-block:: python

    import paddle
    import paddle.fluid as fluid
    from paddle.fluid.param_attr import ParamAttr
    from paddleslim.analysis import model_size
    paddle.enable_static() 
    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
---------------------

.. py:class:: paddleslim.analysis.TableLatencyEvaluator(table_file, delimiter=",")

`源代码 <https://github.com/PaddlePaddle/PaddleSlim/blob/develop/paddleslim/analysis/latency.py>`_

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。

   .. py:method:: 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。
