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spatial_pyramid_pooling_2d.py
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spatial_pyramid_pooling_2d.py
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import math
import six
import chainer
def spatial_pyramid_pooling_2d(x, pyramid_height, pooling=None):
"""Spatial pyramid pooling function.
It outputs a fixed-length vector regardless of input feature map size.
It performs pooling operation to the input 4D-array ``x`` with different
kernel sizes and padding sizes, and then flattens all dimensions except
first dimension of all pooling results, and finally concatenates them along
second dimension.
At :math:`i`-th pyramid level, the kernel size
:math:`(k_h^{(i)}, k_w^{(i)})` and padding size
:math:`(p_h^{(i)}, p_w^{(i)})` of pooling operation are calculated as
below:
.. math::
k_h^{(i)} &= \\lceil b_h / 2^i \\rceil, \\\\
k_w^{(i)} &= \\lceil b_w / 2^i \\rceil, \\\\
p_h^{(i)} &= (2^i k_h^{(i)} - b_h) / 2, \\\\
p_w^{(i)} &= (2^i k_w^{(i)} - b_w) / 2,
where :math:`\\lceil \\cdot \\rceil` denotes the ceiling function, and
:math:`b_h, b_w` are height and width of input variable ``x``,
respectively. Note that index of pyramid level :math:`i` is zero-based.
See detail in paper: `Spatial Pyramid Pooling in Deep Convolutional \
Networks for Visual Recognition \
<https://arxiv.org/abs/1406.4729>`_.
Args:
x (~chainer.Variable): Input variable. The shape of ``x`` should be
``(batchsize, # of channels, height, width)``.
pyramid_height (int): Number of pyramid levels
pooling (str):
Currently, only ``max`` is supported, which performs a 2d max
pooling operation.
Returns:
~chainer.Variable: Output variable. The shape of the output variable
will be :math:`(batchsize, c \\sum_{h=0}^{H-1} 2^{2h}, 1, 1)`,
where :math:`c` is the number of channels of input variable ``x``
and :math:`H` is the number of pyramid levels.
"""
bottom_c, bottom_h, bottom_w = x.shape[1:]
ys = []
# create pooling functions for different pyramid levels and apply it
for pyramid_level in six.moves.range(pyramid_height):
num_bins = int(2 ** pyramid_level)
ksize_h = int(math.ceil(bottom_h / (float(num_bins))))
remainder_h = ksize_h * num_bins - bottom_h
pad_h = remainder_h // 2
ksize_w = int(math.ceil(bottom_w / (float(num_bins))))
remainder_w = ksize_w * num_bins - bottom_w
pad_w = remainder_w // 2
ksize = (ksize_h, ksize_w)
pad = (pad_h, pad_w)
if pooling != 'max':
raise ValueError('Unsupported pooling operation: ', pooling)
y_var = chainer.functions.max_pooling_2d(
x, ksize=ksize, stride=None, pad=pad, cover_all=True)
n, c, h, w = y_var.shape
ys.append(y_var.reshape((n, c * h * w, 1, 1)))
return chainer.functions.concat(ys)