def avg_pool(
data_batch, # type: Node
window_shape, # type: TensorShape
window_strides=None, # type: List[int]
padding_below=None, # type: TensorShape
padding_above=None, # type: TensorShape
include_padding=False, # type: bool
name=None, # type: str
):
# type: (...) -> Node
"""Return average pooling node.
:param data_batch: The input node providing data.
:param window_shape: The pooling window shape.
:param window_strides: The window movement strides.
:param padding_below: The input data optional padding below filled with zeros.
:param padding_above: The input data optional padding below filled with zeros.
:param include_padding: Whether or not to include zero padding in average computations.
:param name: Optional name for the new output node.
:return: New node with AvgPool operation applied on its data.
"""
spatial_dim_count = len(window_shape)
if window_strides is None:
window_strides = [1] * spatial_dim_count
if padding_above is None:
padding_above = [0] * spatial_dim_count
if padding_below is None:
padding_below = [0] * spatial_dim_count
return AvgPool(data_batch, Shape(window_shape), Strides(window_strides),
Shape(padding_below), Shape(padding_above), include_padding)
def avg_pool(x, # type: Node
window_shape, # type: TensorShape
strides=None, # type: List[int]
padding_above=None, # type: List[int]
padding_below=None, # type: List[int]
zero_pad=True, # type: bool
name=None, # type: str
):
# type: (...) -> Node
"""Return average pooling node."""
if strides is None:
strides = [1] * len(window_shape) # Default to as many 1s as spatial dimensions of input.
if padding_above is None:
padding_above = [0] * len(window_shape)
if padding_below is None:
padding_below = [0] * len(window_shape)
return AvgPool(x, Shape(window_shape), Strides(strides),
Shape(padding_above), Shape(padding_below), zero_pad)
