/
average_pooling_2d.py
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/
average_pooling_2d.py
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import numpy
import chainer
from chainer.backends import cuda
from chainer.backends import intel64
from chainer import function_node
from chainer.functions.pooling import pooling_2d
from chainer.utils import conv
class AveragePooling2D(pooling_2d.Pooling2D):
"""Average pooling over a set of 2d planes."""
# TODO(beam2d): Support cover_all mode.
def forward_cpu(self, x):
if (intel64.should_use_ideep('>=auto')
and intel64.inputs_all_ready(x)):
return self._forward_ideep(x)
self._in_shape = x[0].shape
self._in_dtype = x[0].dtype
col = conv.im2col_cpu(x[0], self.kh, self.kw, self.sy, self.sx,
self.ph, self.pw)
y = col.mean(axis=(2, 3))
return y,
def _forward_ideep(self, x):
self._in_shape = x[0].shape
self._in_dtype = x[0].dtype
n, c, h, w = x[0].shape
y_h = conv.get_conv_outsize(
h, self.kh, self.sy, self.ph, self.cover_all)
assert y_h > 0, 'Height in the output should be positive.'
y_w = conv.get_conv_outsize(
w, self.kw, self.sx, self.pw, self.cover_all)
assert y_w > 0, 'Width in the output should be positive.'
pd = self.sy * (y_h - 1) + self.kh - h - self.ph
pr = self.sx * (y_w - 1) + self.kw - w - self.pw
pp = intel64.ideep.pooling2DParam(
(n, c, y_h, y_w),
self.kh, self.kw,
self.sy, self.sx,
self.ph, self.pw,
pd, pr,
intel64.ideep.pooling2DParam.pooling_avg_include_padding)
y, = intel64.ideep.pooling2D.Forward(intel64.ideep.array(x[0]), pp)
return y,
def forward_gpu(self, x):
if chainer.should_use_cudnn('>=auto'):
self.retain_inputs((0,))
return super(AveragePooling2D, self).forward_gpu(x)
self._in_shape = x[0].shape
self._in_dtype = x[0].dtype
n, c, h, w = x[0].shape
y_h = conv.get_conv_outsize(h, self.kh, self.sy, self.ph)
y_w = conv.get_conv_outsize(w, self.kw, self.sx, self.pw)
y = cuda.cupy.empty((n, c, y_h, y_w), dtype=x[0].dtype)
coeff = 1. / (self.kh * self.kw)
kern = cuda.elementwise(
'raw T in, int32 h, int32 w,'
'int32 out_h, int32 out_w, int32 kh, int32 kw,'
'int32 sy, int32 sx, int32 ph, int32 pw, T coeff',
'T out', '''
int c0 = i / (out_h * out_w);
int out_y = i / out_w % out_h;
int out_x = i % out_w;
int in_y_0 = max(0, out_y * sy - ph);
int in_y_1 = min(h, out_y * sy + kh - ph);
int in_x_0 = max(0, out_x * sx - pw);
int in_x_1 = min(w, out_x * sx + kw - pw);
T val = 0;
for (int y = in_y_0; y < in_y_1; ++y) {
int offset_y = w * (y + h * c0);
for (int x = in_x_0; x < in_x_1; ++x) {
val = val + in[x + offset_y];
}
}
out = val * coeff;
''', 'avg_pool_fwd')
kern(x[0].reduced_view(), h, w, y_h, y_w, self.kh, self.kw,
self.sy, self.sx, self.ph, self.pw, coeff, y)
return y,
def backward(self, indexes, gy):
return AveragePooling2DGrad(self).apply(gy)
def create_pool_desc(self):
return cuda.cudnn.create_pooling_descriptor(
(self.kh, self.kw), (self.sy, self.sx), (self.ph, self.pw),
cuda.cuda.cudnn.CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING)
class AveragePooling2DGrad(function_node.FunctionNode):
def __init__(self, apool2d):
self.kh = apool2d.kh
self.kw = apool2d.kw
self.sy = apool2d.sy
self.sx = apool2d.sx
self.ph = apool2d.ph
self.pw = apool2d.pw
self._used_cudnn = apool2d._used_cudnn
if not self._used_cudnn:
self._in_shape = apool2d._in_shape
self._in_dtype = apool2d._in_dtype
self.apool2d = apool2d
def forward_cpu(self, gy):
if (intel64.should_use_ideep('>=auto')
and intel64.inputs_all_ready(gy)):
return self._forward_ideep(gy)
h, w = self._in_shape[2:]
gcol = numpy.tile(gy[0][:, :, None, None],
(1, 1, self.kh, self.kw, 1, 1))
gx = conv.col2im_cpu(gcol, self.sy, self.sx, self.ph, self.pw, h, w)
gx /= self.kh * self.kw
return gx,
def _forward_ideep(self, gy):
n, c, h, w = self._in_shape
y_h, y_w = gy[0].shape[2:]
pd = self.sy * (y_h - 1) + self.kh - h - self.ph
pr = self.sx * (y_w - 1) + self.kw - w - self.pw
pp = intel64.ideep.pooling2DParam(
self._in_shape,
self.kh, self.kw,
self.sy, self.sx,
self.ph, self.pw,
pd, pr,
intel64.ideep.pooling2DParam.pooling_avg_include_padding)
gx = intel64.ideep.pooling2D.Backward(
intel64.ideep.array(gy[0]), None, pp)
return gx,
def forward_gpu(self, gy):
if self._used_cudnn:
x, = self.apool2d.get_retained_inputs()
return self.apool2d.backward_gpu((x.data,), gy)
n, c, h, w = self._in_shape
y_h, y_w = gy[0].shape[2:]
gx = cuda.cupy.empty(self._in_shape, self._in_dtype)
coeff = 1. / (self.kh * self.kw)
cuda.elementwise(
'raw T gy, int32 h, int32 w,'
'int32 out_h, int32 out_w, int32 kh, int32 kw,'
'int32 sy, int32 sx, int32 ph, int32 pw, T coeff',
'T gx',
'''
int c0 = i / (h * w);
int y = i / w % h + ph;
int x = i % w + pw;
int out_y_0 = max(0, (y - kh + sy) / sy);
int out_y_1 = min(out_h, (y + sy) / sy);
int out_x_0 = max(0, (x - kw + sx) / sx);
int out_x_1 = min(out_w, (x + sx) / sx);
int hc0 = out_h * c0;
T val = 0;
for (int out_y = out_y_0; out_y < out_y_1; ++out_y) {
for (int out_x = out_x_0; out_x < out_x_1; ++out_x) {
val = val + gy[out_x + out_w * (out_y + hc0)];
}
}
gx = val * coeff;
''', 'avg_pool_bwd')(gy[0].reduced_view(),
h, w, y_h, y_w, self.kh, self.kw,
self.sy, self.sx, self.ph, self.pw, coeff,
gx)
return gx,
def backward(self, indexes, grad_outputs):
return AveragePooling2D(
(self.kh, self.kw), (self.sy, self.sx), (self.ph, self.pw),
False).apply(grad_outputs)
def average_pooling_2d(x, ksize, stride=None, pad=0):
"""Spatial average pooling function.
This function acts similarly to :class:`~functions.Convolution2D`, but
it computes the average of input spatial patch for each channel
without any parameter instead of computing the inner products.
Args:
x (~chainer.Variable): Input variable.
ksize (int or pair of ints): Size of pooling window. ``ksize=k`` and
``ksize=(k, k)`` are equivalent.
stride (int or pair of ints or None): Stride of pooling applications.
``stride=s`` and ``stride=(s, s)`` are equivalent. If ``None`` is
specified, then it uses same stride as the pooling window size.
pad (int or pair of ints): Spatial padding width for the input array.
``pad=p`` and ``pad=(p, p)`` are equivalent.
Returns:
~chainer.Variable: Output variable.
.. note::
This function currently does not support ``cover_all`` mode as
:func:`max_pooling_2d`. Average pooling runs in non-cover-all mode.
"""
return AveragePooling2D(ksize, stride, pad, False).apply((x,))[0]