/
max_pooling_2d.py
382 lines (329 loc) · 14.6 KB
/
max_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 MaxPooling2D(pooling_2d.Pooling2D):
"""Max pooling over a set of 2d planes."""
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,
pval=-float('inf'), cover_all=self.cover_all)
n, c, kh, kw, out_h, out_w = col.shape
col = col.reshape(n, c, kh * kw, out_h, out_w)
# We select maximum twice, since the implementation using numpy.choose
# hits its bug when kh * kw >= 32.
self.indexes = col.argmax(axis=2)
y = col.max(axis=2)
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.'
self.pd = self.sy * (y_h - 1) + self.kh - h - self.ph
self.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,
self.pd, self.pr,
intel64.ideep.pooling2DParam.pooling_max)
y, self.indexes = 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(MaxPooling2D, 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, 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.'
y = cuda.cupy.empty((n, c, y_h, y_w), dtype=x[0].dtype)
self.indexes = cuda.cupy.empty((n, c, y_h, y_w), dtype=numpy.int32)
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 out, S indexes',
'''
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 maxval = in[in_x_0 + w * (in_y_0 + h * c0)];
int argmax_y = in_y_0;
int argmax_x = in_x_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) {
float v = in[x + offset_y];
if (maxval < v) {
maxval = v;
argmax_y = y;
argmax_x = x;
}
}
}
out = maxval;
int argmax_ky = argmax_y + ph - out_y * sy;
int argmax_kx = argmax_x + pw - out_x * sx;
indexes = argmax_kx + kw * argmax_ky;
''', 'max_pool_fwd')(x[0].reduced_view(),
h, w, y_h, y_w, self.kh, self.kw,
self.sy, self.sx, self.ph, self.pw,
y, self.indexes)
return y,
def backward(self, indexes, gy):
return MaxPooling2DGrad(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_MAX)
class MaxPooling2DGrad(function_node.FunctionNode):
def __init__(self, mpool2d):
self.kh = mpool2d.kh
self.kw = mpool2d.kw
self.sy = mpool2d.sy
self.sx = mpool2d.sx
self.ph = mpool2d.ph
self.pw = mpool2d.pw
self.cover_all = mpool2d.cover_all
self._used_cudnn = mpool2d._used_cudnn
if not self._used_cudnn:
self.indexes = mpool2d.indexes
self._in_shape = mpool2d._in_shape
self._in_dtype = mpool2d._in_dtype
self.mpool2d = mpool2d
def forward_cpu(self, gy):
if (intel64.should_use_ideep('>=auto')
and intel64.inputs_all_ready(gy)):
return self._forward_ideep(gy)
n, c, out_h, out_w = gy[0].shape
h, w = self._in_shape[2:]
kh, kw = self.kh, self.kw
gcol = numpy.zeros(
(n * c * out_h * out_w * kh * kw), dtype=self._in_dtype)
indexes = self.indexes.flatten()
indexes += numpy.arange(0, indexes.size * kh * kw, kh * kw)
gcol[indexes] = gy[0].ravel()
gcol = gcol.reshape(n, c, out_h, out_w, kh, kw)
gcol = numpy.swapaxes(gcol, 2, 4)
gcol = numpy.swapaxes(gcol, 3, 5)
gx = conv.col2im_cpu(gcol, self.sy, self.sx, self.ph, self.pw, h, w)
return gx,
def _forward_ideep(self, gy):
# FIXME
# Here we expect indexes is returned from MKL-DNN
# otherwise, there are dtype mismatch for reorder (int64-->uint8)
if not isinstance(self.indexes, intel64.ideep.mdarray):
return self.forward_cpu(gy)
n, c, h, w = self._in_shape
y_h, y_w = gy[0].shape[2:]
self.pd = self.sy * (y_h - 1) + self.kh - h - self.ph
self.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,
self.pd, self.pr,
intel64.ideep.pooling2DParam.pooling_max)
self.indexes = intel64.ideep.array(self.indexes)
gx = intel64.ideep.pooling2D.Backward(
intel64.ideep.array(gy[0]),
self.indexes, pp)
return gx,
def forward_gpu(self, gy):
if self._used_cudnn:
x, = self.mpool2d.get_retained_inputs()
return self.mpool2d.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)
cuda.elementwise(
'raw T gy, raw S indexes, int32 h, int32 w,'
'int32 out_h, int32 out_w, int32 kh, int32 kw,'
'int32 sy, int32 sx, int32 ph, int32 pw',
'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);
T val = 0;
for (int out_y = out_y_0; out_y < out_y_1; ++out_y) {
int ky = y - out_y * sy;
for (int out_x = out_x_0; out_x < out_x_1; ++out_x) {
int kx = x - out_x * sx;
int offset = out_x + out_w * (out_y + out_h * c0);
if (indexes[offset] == kx + kw * ky) {
val = val + gy[offset];
}
}
}
gx = val;
''',
'max_pool_bwd')(gy[0].reduced_view(), self.indexes.reduced_view(),
h, w, y_h, y_w, self.kh, self.kw,
self.sy, self.sx, self.ph, self.pw,
gx)
return gx,
def backward(self, indexes, ggx):
return MaxPooling2DWithIndexes(self.mpool2d).apply(ggx)
class MaxPooling2DWithIndexes(function_node.FunctionNode):
def __init__(self, mpool2d):
self.kh = mpool2d.kh
self.kw = mpool2d.kw
self.sy = mpool2d.sy
self.sx = mpool2d.sx
self.ph = mpool2d.ph
self.pw = mpool2d.pw
self.cover_all = mpool2d.cover_all
self._used_cudnn = mpool2d._used_cudnn
if not self._used_cudnn:
self.indexes = mpool2d.indexes
else:
self.mpool2d = mpool2d
def forward_cpu(self, x):
col = conv.im2col_cpu(
x[0], self.kh, self.kw, self.sy, self.sx, self.ph, self.pw,
pval=-float('inf'), cover_all=self.cover_all)
n, c, kh, kw, out_h, out_w = col.shape
col = col.reshape(n, c, kh * kw, out_h, out_w)
col = col.transpose(0, 1, 3, 4, 2).reshape(-1, kh * kw)
indexes = self.indexes.ravel()
col = col[numpy.arange(len(indexes)), indexes]
return col.reshape(n, c, out_h, out_w),
def forward_gpu(self, inputs):
if self._used_cudnn:
x, = self.mpool2d.get_retained_inputs()
return self._forward_gpu_compute_indexes_again((x.data, inputs[0]))
else:
x, = inputs
n, c, h, w = x.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.'
y = cuda.cupy.empty((n, c, y_h, y_w), dtype=x.dtype)
cuda.elementwise(
'raw T in, raw S indexes, int32 h, int32 w, int32 out_h,'
'int32 out_w, int32 kh, int32 kw, int32 sy, int32 sx,'
'int32 ph, int32 pw', 'T out',
'''
int c0 = i / (out_h * out_w);
int out_y = i / out_w % out_h;
int out_x = i % out_w;
int index = indexes[i];
int max_y = max(0, out_y * sy - ph + index / kw);
int max_x = max(0, out_x * sx - pw + index % kw);
out = in[max_x + w * (max_y + h * c0)];
''', 'max_pool_grad_fwd')(
x.reduced_view(), self.indexes.reduced_view(), h, w,
y_h, y_w, self.kh, self.kw, self.sy, self.sx, self.ph,
self.pw, y)
return y,
def _forward_gpu_compute_indexes_again(self, inputs):
x, ggx = inputs
n, c, h, w = ggx.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.'
y = cuda.cupy.empty((n, c, y_h, y_w), dtype=x.dtype)
cuda.elementwise(
'raw T in, raw T ggx, int32 h, int32 w, int32 out_h,'
'int32 out_w, int32 kh, int32 kw, int32 sy, int32 sx,'
'int32 ph, int32 pw', '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 maxval = in[in_x_0 + w * (in_y_0 + h * c0)];
int argmax_y = in_y_0;
int argmax_x = in_x_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) {
float v = in[x + offset_y];
if (maxval < v) {
argmax_y = y;
argmax_x = x;
}
}
}
out = ggx[argmax_x + w * (argmax_y + h * c0)]
''', 'max_pool_grad_fwd_calc_indexes')(
x.reduced_view(), ggx.reduced_view(), h, w, y_h, y_w, self.kh,
self.kw, self.sy, self.sx, self.ph, self.pw, y)
return y,
def max_pooling_2d(x, ksize, stride=None, pad=0, cover_all=True,
return_indices=False):
"""Spatial max pooling function.
This function acts similarly to :class:`~functions.Convolution2D`, but
it computes the maximum 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.
cover_all (bool): If ``True``, all spatial locations are pooled into
some output pixels. It may make the output size larger.
return_indices (bool): If ``True``, pooling indices array is returned
together with the output variable. The returned indices are
expected for use by :func:`chainer.functions.upsampling_2d`.
Note that cuDNN will not be used for this function if
``return_indices`` is set to ``True``, as cuDNN does not return
indices information.
Returns:
~chainer.Variable or tuple:
When ``return_indices`` is ``False`` (default), returns the output
variable.
When ``True``, returns the tuple of the output variable and
pooling indices (`ndarray`). Pooling indices will be on the same
device as the input.
"""
func = MaxPooling2D(ksize, stride, pad, cover_all)
if return_indices:
with chainer.using_config('use_cudnn', 'never'):
out = func.apply((x,))[0]
return out, func.indexes
return func.apply((x,))[0]