def max_pool(value, ksize, strides, padding, data_format='NHWC', name=None):
"""Perform max pooling on spatial axes.
Parameters
----------
value : Tensor
The input tensor.
ksize : list of int
The kernel size with length >= 4.
strides : list of int
The strides with length >= 4.
padding : str
The padding algorithm. ``VALID`` or ``SAME``.
data_format : str
The data format. ``NHWC`` or ``NCHW``.
name : None or str
The optional name of op.
Returns
-------
Tensor
The output tensor.
"""
if len(ksize) < 4:
raise ValueError('ksize must be a list with length >=4.')
if len(strides) < 4:
raise ValueError('strides must be a list with length >=4.')
if len(ksize) != len(strides):
raise ValueError('ksize and strides should have the same length.')
if len(ksize) == 4:
if data_format == 'NHWC':
if ksize[0] != 1 or ksize[3] != 1 or strides[0] != 1 or strides[
3] != 1:
raise ValueError(
'The pooling can only be performed on spatial axes.')
return ops.Pool2d(value, [ksize[1], ksize[2]],
[strides[1], strides[2]],
padding=padding,
data_format=data_format,
mode='MAX')
if data_format == 'NCHW':
if ksize[0] != 1 or ksize[1] != 1 or strides[0] != 1 or strides[
1] != 1:
raise ValueError(
'The pooling can only be performed on spatial axes.')
return ops.Pool2d(value, [ksize[2], ksize[3]],
[strides[2], strides[3]],
padding=padding,
data_format=data_format,
mode='MAX')
else:
raise NotImplementedError(
'Pool{}d has not been implemented yet.'.format(len(ksize) - 2))
def Setup(self, bottom):
input = bottom[0] if isinstance(bottom, list) else bottom
super(PoolingLayer, self).Setup(bottom)
return ops.Pool2d(input, **self._param)
def LayerSetup(self, bottom):
return _ops.Pool2d(bottom, **self.arguments)