slim.arg_scope（）的使用

【https://blog.csdn.net/u013921430 转载】

slim是一种轻量级的tensorflow库，可以使模型的构建，训练，测试都变得更加简单。在slim库中对很多常用的函数进行了定义，slim.arg_scope（）是slim库中经常用到的函数之一。函数的定义如下；

@tf_contextlib.contextmanager





def arg_scope(list_ops_or_scope, **kwargs):





  """Stores the default arguments for the given set of list_ops.











  For usage, please see examples at top of the file.











  Args:





    list_ops_or_scope: List or tuple of operations to set argument scope for or





      a dictionary containing the current scope. When list_ops_or_scope is a





      dict, kwargs must be empty. When list_ops_or_scope is a list or tuple,





      then every op in it need to be decorated with @add_arg_scope to work.





    **kwargs: keyword=value that will define the defaults for each op in





              list_ops. All the ops need to accept the given set of arguments.











  Yields:





    the current_scope, which is a dictionary of {op: {arg: value}}





  Raises:





    TypeError: if list_ops is not a list or a tuple.





    ValueError: if any op in list_ops has not be decorated with @add_arg_scope.





  """





  if isinstance(list_ops_or_scope, dict):





    # Assumes that list_ops_or_scope is a scope that is being reused.





    if kwargs:





      raise ValueError('When attempting to re-use a scope by suppling a'





                       'dictionary, kwargs must be empty.')





    current_scope = list_ops_or_scope.copy()





    try:





      _get_arg_stack().append(current_scope)





      yield current_scope





    finally:





      _get_arg_stack().pop()





  else:





    # Assumes that list_ops_or_scope is a list/tuple of ops with kwargs.





    if not isinstance(list_ops_or_scope, (list, tuple)):





      raise TypeError('list_ops_or_scope must either be a list/tuple or reused'





                      'scope (i.e. dict)')





    try:





      current_scope = current_arg_scope().copy()





      for op in list_ops_or_scope:





        key_op = _key_op(op)





        if not has_arg_scope(op):





          raise ValueError('%s is not decorated with @add_arg_scope',





                           _name_op(op))





        if key_op in current_scope:





          current_kwargs = current_scope[key_op].copy()





          current_kwargs.update(kwargs)





          current_scope[key_op] = current_kwargs





        else:





          current_scope[key_op] = kwargs.copy()





      _get_arg_stack().append(current_scope)





      yield current_scope





    finally:





      _get_arg_stack().pop()

如注释中所说，这个函数的作用是给list_ops中的内容设置默认值。但是每个list_ops中的每个成员需要用@add_arg_scope修饰才行。所以使用slim.arg_scope（）有两个步骤：

1. 使用@slim.add_arg_scope修饰目标函数
2. 用 slim.arg_scope（）为目标函数设置默认参数.

例如如下代码；首先用@slim.add_arg_scope修饰目标函数fun1（），然后利用slim.arg_scope（）为它设置默认参数。

import tensorflow as tf





slim =tf.contrib.slim





 





@slim.add_arg_scope





def fun1(a=0,b=0):





    return (a+b)





 





with slim.arg_scope([fun1],a=10):





    x=fun1(b=30)





    print(x)

运行结果为：

40

平常所用到的slim.conv2d( ),slim.fully_connected( ),slim.max_pool2d( )等函数在他被定义的时候就已经添加了@add_arg_scope。以slim.conv2d( )为例；

@add_arg_scope





def convolution(inputs,





                num_outputs,





                kernel_size,





                stride=1,





                padding='SAME',





                data_format=None,





                rate=1,





                activation_fn=nn.relu,





                normalizer_fn=None,





                normalizer_params=None,





                weights_initializer=initializers.xavier_initializer(),





                weights_regularizer=None,





                biases_initializer=init_ops.zeros_initializer(),





                biases_regularizer=None,





                reuse=None,





                variables_collections=None,





                outputs_collections=None,





                trainable=True,





                scope=None):

所以，在使用过程中可以直接slim.conv2d( )等函数设置默认参数。例如在下面的代码中，不做单独声明的情况下，slim.conv2d, slim.max_pool2d, slim.avg_pool2d三个函数默认的步长都设为1，padding模式都是'VALID'的。但是也可以在调用时进行单独声明。这种参数设置方式在构建网络模型时，尤其是较深的网络时，可以节省时间。

 with slim.arg_scope(





                [slim.conv2d, slim.max_pool2d, slim.avg_pool2d],stride = 1, padding = 'VALID'):





            net = slim.conv2d(inputs, 32, [3, 3], stride = 2, scope = 'Conv2d_1a_3x3')





            net = slim.conv2d(net, 32, [3, 3], scope = 'Conv2d_2a_3x3')





            net = slim.conv2d(net, 64, [3, 3], padding = 'SAME', scope = 'Conv2d_2b_3x3')

---

@修饰符

其实这种用法是python中常用到的。在python中@修饰符放在函数定义的上方，它将被修饰的函数作为参数，并返回修饰后的同名函数。形式如下；

@fun_a     #等价于fun_a(fun_b)





def fun_b():

这在本质上讲跟直接调用被修饰的函数没什么区别，但是有时候也有用处，例如在调用被修饰函数前需要输出时间信息，我们可以在@后方的函数中添加输出时间信息的语句，这样每次我们只需要调用@后方的函数即可。

def funs(fun,factor=20):





    x=fun()





    print(factor*x)





    





    





@funs     #等价funs(add(),fator=20)





def add(a=10,b=20):





    return(a+b)