"max_pool2d dans le module tensorflow.contrib.framework.python.ops.arg_scope:" Où puis-je trouver le code de max_pool2d?

Question

import tensorflow as tf 
slim = tf.contrib.slim 
slim.max_pool2d(inputs,[1,1],stride=factor,scope=scope) 

Je veux voir le code interne de max_pool2d. Où pourrais-je le trouver?

Answer 1

layers.py:2097:

@add_arg_scope 
def max_pool2d(inputs, 
       kernel_size, 
       stride=2, 
       padding='VALID', 
       data_format=DATA_FORMAT_NHWC, 
       outputs_collections=None, 
       scope=None): 
    """Adds a 2D Max Pooling op. 

    It is assumed that the pooling is done per image but not in batch or channels. 

    Args: 
    inputs: A 4-D tensor of shape `[batch_size, height, width, channels]` if 
     `data_format` is `NHWC`, and `[batch_size, channels, height, width]` if 
     `data_format` is `NCHW`. 
    kernel_size: A list of length 2: [kernel_height, kernel_width] of the 
     pooling kernel over which the op is computed. Can be an int if both 
     values are the same. 
    stride: A list of length 2: [stride_height, stride_width]. 
     Can be an int if both strides are the same. Note that presently 
     both strides must have the same value. 
    padding: The padding method, either 'VALID' or 'SAME'. 
    data_format: A string. `NHWC` (default) and `NCHW` are supported. 
    outputs_collections: The collections to which the outputs are added. 
    scope: Optional scope for name_scope. 

    Returns: 
    A `Tensor` representing the results of the pooling operation. 

    Raises: 
    ValueError: If `data_format` is neither `NHWC` nor `NCHW`. 
    ValueError: If 'kernel_size' is not a 2-D list 
    """ 
    if data_format not in (DATA_FORMAT_NCHW, DATA_FORMAT_NHWC): 
    raise ValueError('data_format has to be either NCHW or NHWC.') 
    with ops.name_scope(scope, 'MaxPool2D', [inputs]) as sc: 
    inputs = ops.convert_to_tensor(inputs) 
    df = ('channels_first' if data_format and data_format.startswith('NC') 
      else 'channels_last') 
    layer = pooling_layers.MaxPooling2D(pool_size=kernel_size, 
             strides=stride, 
             padding=padding, 
             data_format=df, 
             _scope=sc) 
    outputs = layer.apply(inputs) 
    return utils.collect_named_outputs(outputs_collections, sc, outputs)