def perform(self, node, inp, out):
x, = inp
z, = out
if len(x.shape) != 4:
raise NotImplementedError(
'Pool requires 4D input for now')
z_shape = self.out_shape(x.shape, self.ds, self.ignore_border, self.st,
self.padding)
if not self.ignore_border:
assert z_shape[2] > 0
assert z_shape[3] > 0
if (z[0] is None) or (z[0].shape != z_shape):
z[0] = numpy.empty(z_shape, dtype=x.dtype)
zz = z[0]
# number of pooling output rows
pr = zz.shape[-2]
# number of pooling output cols
pc = zz.shape[-1]
ds0, ds1 = self.ds
st0, st1 = self.st
pad_h = self.padding[0]
pad_w = self.padding[1]
img_rows = x.shape[-2] + 2 * pad_h
img_cols = x.shape[-1] + 2 * pad_w
inc_pad = self.mode == 'average_inc_pad'
# pad the image
if self.padding != (0, 0):
y = numpy.zeros(
(x.shape[0], x.shape[1], img_rows, img_cols),
dtype=x.dtype)
y[:, :, pad_h:(img_rows - pad_h), pad_w:(img_cols - pad_w)] = x
else:
y = x
func = numpy.max
if self.mode == 'sum':
func = numpy.sum
elif self.mode != 'max':
func = numpy.average
for n in xrange(x.shape[0]):
for k in xrange(x.shape[1]):
for r in xrange(pr):
row_st = r * st0
row_end = builtins.min(row_st + ds0, img_rows)
if not inc_pad:
row_st = builtins.max(row_st, self.padding[0])
row_end = builtins.min(row_end, x.shape[-2] + pad_h)
for c in xrange(pc):
col_st = c * st1
col_end = builtins.min(col_st + ds1, img_cols)
if not inc_pad:
col_st = builtins.max(col_st, self.padding[1])
col_end = builtins.min(col_end,
x.shape[-1] + pad_w)
zz[n, k, r, c] = func(y[
n, k, row_st:row_end, col_st:col_end])
def perform(self, node, inp, out):
x, = inp
z, = out
if len(x.shape) != 4:
raise NotImplementedError(
'Pool requires 4D input for now')
z_shape = self.out_shape(x.shape, self.ds, self.ignore_border, self.st,
self.padding)
if not self.ignore_border:
assert z_shape[2] > 0
assert z_shape[3] > 0
if (z[0] is None) or (z[0].shape != z_shape):
z[0] = numpy.empty(z_shape, dtype=x.dtype)
zz = z[0]
# number of pooling output rows
pr = zz.shape[-2]
# number of pooling output cols
pc = zz.shape[-1]
ds0, ds1 = self.ds
st0, st1 = self.st
pad_h = self.padding[0]
pad_w = self.padding[1]
img_rows = x.shape[-2] + 2 * pad_h
img_cols = x.shape[-1] + 2 * pad_w
inc_pad = self.mode == 'average_inc_pad'
# pad the image
if self.padding != (0, 0):
y = numpy.zeros(
(x.shape[0], x.shape[1], img_rows, img_cols),
dtype=x.dtype)
y[:, :, pad_h:(img_rows - pad_h), pad_w:(img_cols - pad_w)] = x
else:
y = x
func = numpy.max
if self.mode == 'sum':
func = numpy.sum
elif self.mode != 'max':
func = numpy.average
for n in xrange(x.shape[0]):
for k in xrange(x.shape[1]):
for r in xrange(pr):
row_st = r * st0
row_end = builtins.min(row_st + ds0, img_rows)
if not inc_pad:
row_st = builtins.max(row_st, self.padding[0])
row_end = builtins.min(row_end, x.shape[-2] + pad_h)
for c in xrange(pc):
col_st = c * st1
col_end = builtins.min(col_st + ds1, img_cols)
if not inc_pad:
col_st = builtins.max(col_st, self.padding[1])
col_end = builtins.min(col_end,
x.shape[-1] + pad_w)
zz[n, k, r, c] = func(y[
n, k, row_st:row_end, col_st:col_end])
def numpy_max_pool_2d_stride_padding(x,
ds,
ignore_border=True,
st=None,
padding=(0, 0),
mode='max'):
assert ignore_border
pad_h = padding[0]
pad_w = padding[1]
h = x.shape[-2]
w = x.shape[-1]
assert ds[0] > pad_h
assert ds[1] > pad_w
def pad_img(x):
y = numpy.zeros((x.shape[0], x.shape[1], x.shape[2] + pad_h * 2,
x.shape[3] + pad_w * 2),
dtype=x.dtype)
y[:, :, pad_h:(x.shape[2] + pad_h), pad_w:(x.shape[3] + pad_w)] = x
return y
img_rows = h + 2 * pad_h
img_cols = w + 2 * pad_w
out_r = (img_rows - ds[0]) // st[0] + 1
out_c = (img_cols - ds[1]) // st[1] + 1
out_shp = list(x.shape[:-2])
out_shp.append(out_r)
out_shp.append(out_c)
ds0, ds1 = ds
st0, st1 = st
output_val = numpy.zeros(out_shp)
y = pad_img(x)
func = numpy.max
if mode == 'sum':
func = numpy.sum
elif mode != 'max':
func = numpy.average
inc_pad = mode == 'average_inc_pad'
for k in numpy.ndindex(*x.shape[:-2]):
for i in range(output_val.shape[-2]):
ii_st = i * st[0]
ii_end = builtins.min(ii_st + ds[0], img_rows)
if not inc_pad:
ii_st = builtins.max(ii_st, pad_h)
ii_end = builtins.min(ii_end, h + pad_h)
for j in range(output_val.shape[-1]):
jj_st = j * st[1]
jj_end = builtins.min(jj_st + ds[1], img_cols)
if not inc_pad:
jj_st = builtins.max(jj_st, pad_w)
jj_end = builtins.min(jj_end, w + pad_w)
patch = y[k][ii_st:ii_end, jj_st:jj_end]
output_val[k][i, j] = func(patch)
return output_val
def perform(self, node, inp, out):
x, = inp
z, ind = out
ind = numpy.zeros_like(x)
if len(x.shape) != 4:
raise NotImplementedError('Pool requires 4D input for now')
z_shape = self.out_shape(x.shape, self.ds, self.ignore_border, self.st,
self.padding)
if (z[0] is None) or (z[0].shape != z_shape):
z[0] = numpy.empty(z_shape, dtype=x.dtype)
zz = z[0]
# number of pooling output rows
pr = zz.shape[-2]
# number of pooling output cols
pc = zz.shape[-1]
ds0, ds1 = self.ds
st0, st1 = self.st
pad_h = self.padding[0]
pad_w = self.padding[1]
img_rows = x.shape[-2] + 2 * pad_h
img_cols = x.shape[-1] + 2 * pad_w
inc_pad = 0
# pad the image
if self.padding != (0, 0):
y = numpy.zeros(
(x.shape[0], x.shape[1], img_rows, img_cols),
dtype=x.dtype)
y[:, :, pad_h:(img_rows - pad_h), pad_w:(img_cols - pad_w)] = x
else:
y = x
for n in xrange(x.shape[0]):
for k in xrange(x.shape[1]):
for r in xrange(pr):
row_st = r * st0
row_end = builtins.min(row_st + ds0, img_rows)
if not inc_pad:
row_st = builtins.max(row_st, self.padding[0])
row_end = builtins.min(row_end, x.shape[-2] + pad_h)
for c in xrange(pc):
col_st = c * st1
col_end = builtins.min(col_st + ds1, img_cols)
if not inc_pad:
col_st = builtins.max(col_st, self.padding[1])
col_end = builtins.min(col_end,
x.shape[-1] + pad_w)
cur_max = y[n, k, row_st, col_st]
max_r, max_c = row_st, col_st
for rr in xrange(row_st, row_end):
for cc in xrange(col_st, col_end):
if y[n, k, rr, cc] > cur_max:
cur_max = y[n, k, rr, cc]
max_r, max_c = rr, cc
zz[n, k, r, c] = cur_max
ind[n, k, max_r, max_c] = 1
def perform(self, node, inp, out):
if self.mode == 'average_exc_pad' and self.padding != (0, 0):
raise NotImplementedError()
x, gz = inp
gx_stg, = out
z_shape = self.out_shape(x.shape, self.ds, self.ignore_border, self.st,
self.padding)
if (gx_stg[0] is None) or (gx_stg[0].shape != z_shape):
gx_stg[0] = numpy.empty(z_shape, dtype=x.dtype)
zz = gx_stg[0]
# number of pooling output rows
pr = zz.shape[-2]
# number of pooling output cols
pc = zz.shape[-1]
ds0, ds1 = self.ds
st0, st1 = self.st
pad_h = self.padding[0]
pad_w = self.padding[1]
img_rows = x.shape[-2] + 2 * pad_h
img_cols = x.shape[-1] + 2 * pad_w
inc_pad = self.mode == 'average_inc_pad'
sum_mode = self.mode == 'sum'
# pad the image
if self.padding != (0, 0):
y = numpy.zeros(
(x.shape[0], x.shape[1], img_rows, img_cols),
dtype=x.dtype)
y[:, :, pad_h:(img_rows - pad_h), pad_w:(img_cols - pad_w)] = x
else:
y = x
gx = numpy.zeros_like(y)
for n in xrange(x.shape[0]):
for k in xrange(x.shape[1]):
for r in xrange(pr):
if sum_mode or inc_pad:
row_st = r * st0
else:
row_st = builtins.max(r * st0, self.padding[0])
row_end = builtins.min(row_st + ds0, img_rows)
for c in xrange(pc):
if sum_mode or inc_pad:
col_st = c * st1
else:
col_st = builtins.max(c * st1,
self.padding[1])
col_end = builtins.min(col_st + ds1, img_cols)
if sum_mode:
val = gz[n, k, r, c]
else:
val = gz[n, k, r, c] / ((row_end - row_st) *
(col_end - col_st))
gx[n, k, row_st:row_end, col_st:col_end] += val
# unpad the image
gx = gx[:, :, pad_h:(img_rows - pad_h), pad_w:(img_cols - pad_w)]
gx_stg[0] = gx
def perform(self, node, inp, out):
if self.mode == 'average_exc_pad' and self.padding != (0, 0):
raise NotImplementedError()
x, gz = inp
gx_stg, = out
z_shape = self.out_shape(x.shape, self.ds, self.ignore_border, self.st,
self.padding)
if (gx_stg[0] is None) or (gx_stg[0].shape != z_shape):
gx_stg[0] = numpy.empty(z_shape, dtype=x.dtype)
zz = gx_stg[0]
# number of pooling output rows
pr = zz.shape[-2]
# number of pooling output cols
pc = zz.shape[-1]
ds0, ds1 = self.ds
st0, st1 = self.st
pad_h = self.padding[0]
pad_w = self.padding[1]
img_rows = x.shape[-2] + 2 * pad_h
img_cols = x.shape[-1] + 2 * pad_w
inc_pad = self.mode == 'average_inc_pad'
sum_mode = self.mode == 'sum'
# pad the image
if self.padding != (0, 0):
y = numpy.zeros(
(x.shape[0], x.shape[1], img_rows, img_cols),
dtype=x.dtype)
y[:, :, pad_h:(img_rows - pad_h), pad_w:(img_cols - pad_w)] = x
else:
y = x
gx = numpy.zeros_like(y)
for n in xrange(x.shape[0]):
for k in xrange(x.shape[1]):
for r in xrange(pr):
if sum_mode or inc_pad:
row_st = r * st0
else:
row_st = builtins.max(r * st0, self.padding[0])
row_end = builtins.min(row_st + ds0, img_rows)
for c in xrange(pc):
if sum_mode or inc_pad:
col_st = c * st1
else:
col_st = builtins.max(c * st1,
self.padding[1])
col_end = builtins.min(col_st + ds1, img_cols)
if sum_mode:
val = gz[n, k, r, c]
else:
val = gz[n, k, r, c] / ((row_end - row_st) *
(col_end - col_st))
gx[n, k, row_st:row_end, col_st:col_end] += val
# unpad the image
gx = gx[:, :, pad_h:(img_rows - pad_h), pad_w:(img_cols - pad_w)]
gx_stg[0] = gx
def numpy_max_pool_2d_stride_padding(
x, ds, ignore_border=True, st=None, padding=(0, 0), mode='max'):
assert ignore_border
pad_h = padding[0]
pad_w = padding[1]
h = x.shape[-2]
w = x.shape[-1]
assert ds[0] > pad_h
assert ds[1] > pad_w
def pad_img(x):
y = numpy.zeros(
(x.shape[0], x.shape[1],
x.shape[2] + pad_h * 2, x.shape[3] + pad_w * 2),
dtype=x.dtype)
y[:, :, pad_h:(x.shape[2] + pad_h), pad_w:(x.shape[3] + pad_w)] = x
return y
img_rows = h + 2 * pad_h
img_cols = w + 2 * pad_w
out_r = (img_rows - ds[0]) // st[0] + 1
out_c = (img_cols - ds[1]) // st[1] + 1
out_shp = list(x.shape[:-2])
out_shp.append(out_r)
out_shp.append(out_c)
ds0, ds1 = ds
st0, st1 = st
output_val = numpy.zeros(out_shp)
y = pad_img(x)
func = numpy.max
if mode == 'sum':
func = numpy.sum
elif mode != 'max':
func = numpy.average
inc_pad = mode == 'average_inc_pad'
for k in numpy.ndindex(*x.shape[:-2]):
for i in range(output_val.shape[-2]):
ii_st = i * st[0]
ii_end = builtins.min(ii_st + ds[0], img_rows)
if not inc_pad:
ii_st = builtins.max(ii_st, pad_h)
ii_end = builtins.min(ii_end, h + pad_h)
for j in range(output_val.shape[-1]):
jj_st = j * st[1]
jj_end = builtins.min(jj_st + ds[1], img_cols)
if not inc_pad:
jj_st = builtins.max(jj_st, pad_w)
jj_end = builtins.min(jj_end, w + pad_w)
patch = y[k][ii_st:ii_end, jj_st:jj_end]
output_val[k][i, j] = func(patch)
return output_val
def perform(self, node, inp, out):
print("Careful I am using python to compute maxpool gradients")
assert self.mode == 'max'
x, maxout, gz = inp
gx_stg, = out
# number of pooling output rows
pr = maxout.shape[-2]
# number of pooling output cols
pc = maxout.shape[-1]
ds0, ds1 = self.ds
st0, st1 = self.st
pad_h = self.padding[0]
pad_w = self.padding[1]
img_rows = x.shape[-2] + 2 * pad_h
img_cols = x.shape[-1] + 2 * pad_w
# pad the image
if self.padding != (0, 0):
y = np.zeros(
(x.shape[0], x.shape[1], img_rows, img_cols),
dtype=x.dtype)
y[:, :, pad_h:(img_rows - pad_h), pad_w:(img_cols - pad_w)] = x
else:
y = x
gx = np.zeros_like(y)
for n in xrange(x.shape[0]):
for k in xrange(x.shape[1]):
for r in xrange(pr):
row_st = builtins.max(r * st0, self.padding[0])
row_end = builtins.min(row_st + ds0, img_rows)
for c in xrange(pc):
col_st = builtins.max(c * st1, self.padding[1])
col_end = builtins.min(col_st + ds1, img_cols)
break_flag = False
for row_ind in xrange(row_st, row_end):
for col_ind in xrange(col_st, col_end):
if (maxout[n, k, r, c] == y[n, k, row_ind, col_ind]):
gx[n, k, row_ind, col_ind] += gz[n, k, r, c]
# addition here
break_flag = True
break
if break_flag:
break
# unpad the image
gx = gx[:, :, pad_h:(img_rows - pad_h), pad_w:(img_cols - pad_w)]
gx_stg[0] = gx
def restricted(self, min=None, max=None):
"""
Returns the power series restricted to the coefficients starting at
min and going up to, but not including max. If min is not
specified, then it is assumed to be zero. If max is not specified,
then it is assumed to be infinity.
EXAMPLES::
sage: L = LazyPowerSeriesRing(QQ)
sage: a = L([1])
sage: a.restricted().coefficients(10)
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
sage: a.restricted(min=2).coefficients(10)
[0, 0, 1, 1, 1, 1, 1, 1, 1, 1]
sage: a.restricted(max=5).coefficients(10)
[1, 1, 1, 1, 1, 0, 0, 0, 0, 0]
sage: a.restricted(min=2, max=6).coefficients(10)
[0, 0, 1, 1, 1, 1, 0, 0, 0, 0]
"""
from six.moves import builtins
if ((min is None and max is None) or
(max is None and self.get_aorder() >= min)):
return self
return self._new(partial(self._restricted_gen, min, max),
lambda ao: builtins.max(ao, min), self)
def restricted(self, min=None, max=None):
"""
Return the power series restricted to the coefficients starting at
``min`` and going up to, but not including ``max``.
If ``min`` is not specified, then it is assumed to be zero. If
``max`` is not specified, then it is assumed to be infinity.
EXAMPLES::
sage: L = LazyPowerSeriesRing(QQ)
sage: a = L([1])
sage: a.restricted().coefficients(10)
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
sage: a.restricted(min=2).coefficients(10)
[0, 0, 1, 1, 1, 1, 1, 1, 1, 1]
sage: a.restricted(max=5).coefficients(10)
[1, 1, 1, 1, 1, 0, 0, 0, 0, 0]
sage: a.restricted(min=2, max=6).coefficients(10)
[0, 0, 1, 1, 1, 1, 0, 0, 0, 0]
"""
from six.moves import builtins
if ((min is None and max is None) or
(max is None and self.get_aorder() >= min)):
return self
if min is None:
min = 0
return self._new(partial(self._restricted_gen, min, max),
lambda ao: builtins.max(ao, min), self)
def max(xs, y_from_x=lambda x: x):
'''
>>> range(10) | max()
9
>>> range(10) | max(lambda x: -x)
0
'''
return __builtins__.max(y_from_x(x) for x in xs)
def numpy_max_pool_nd_stride_pad(input,
ws,
ignore_border=True,
stride=None,
pad=None,
mode="max"):
assert ignore_border
nd = len(ws)
if pad is None:
pad = (0, ) * nd
if stride is None:
stride = (0, ) * nd
assert len(pad) == len(ws) == len(stride)
assert all(ws[i] > pad[i] for i in range(nd))
def pad_img(x):
# initialize padded input
y = np.zeros(
x.shape[0:-nd] + tuple(x.shape[-nd + i] + pad[i] * 2
for i in range(nd)),
dtype=x.dtype,
)
# place the unpadded input in the center
block = (slice(None), ) * (len(x.shape) - nd) + tuple(
slice(pad[i], x.shape[-nd + i] + pad[i]) for i in range(nd))
y[block] = x
return y
pad_img_shp = list(input.shape[:-nd])
out_shp = list(input.shape[:-nd])
for i in range(nd):
padded_size = input.shape[-nd + i] + 2 * pad[i]
pad_img_shp.append(padded_size)
out_shp.append((padded_size - ws[i]) // stride[i] + 1)
output_val = np.zeros(out_shp)
padded_input = pad_img(input)
func = np.max
if mode == "sum":
func = np.sum
elif mode != "max":
func = np.average
inc_pad = mode == "average_inc_pad"
for l in np.ndindex(*input.shape[:-nd]):
for r in np.ndindex(*output_val.shape[-nd:]):
region = []
for i in range(nd):
r_stride = r[i] * stride[i]
r_end = builtins.min(r_stride + ws[i],
pad_img_shp[-nd + i])
if not inc_pad:
r_stride = builtins.max(r_stride, pad[i])
r_end = builtins.min(r_end,
input.shape[-nd + i] + pad[i])
region.append(slice(r_stride, r_end))
patch = padded_input[l][region]
output_val[l][r] = func(patch)
return output_val
def numpy_max_pool_nd_stride_padding(input,
ds,
ignore_border=True,
st=None,
padding=None,
mode='max'):
assert ignore_border
nd = len(ds)
if padding is None:
padding = (0, ) * nd
if st is None:
st = (0, ) * nd
assert len(padding) == len(ds) == len(st)
assert all(ds[i] > padding[i] for i in range(nd))
def pad_img(x):
# initialize padded input
y = numpy.zeros(x.shape[0:-nd] +
tuple(x.shape[-nd + i] + padding[i] * 2
for i in range(nd)),
dtype=x.dtype)
# place the unpadded input in the center
block = ((slice(None), ) * (len(x.shape) - nd) + tuple(
slice(padding[i], x.shape[-nd + i] + padding[i])
for i in range(nd)))
y[block] = x
return y
pad_img_shp = list(input.shape[:-nd])
out_shp = list(input.shape[:-nd])
for i in range(nd):
padded_size = input.shape[-nd + i] + 2 * padding[i]
pad_img_shp.append(padded_size)
out_shp.append((padded_size - ds[i]) // st[i] + 1)
output_val = numpy.zeros(out_shp)
padded_input = pad_img(input)
func = numpy.max
if mode == 'sum':
func = numpy.sum
elif mode != 'max':
func = numpy.average
inc_pad = mode == 'average_inc_pad'
for l in numpy.ndindex(*input.shape[:-nd]):
for r in numpy.ndindex(*output_val.shape[-nd:]):
region = []
for i in range(nd):
r_st = r[i] * st[i]
r_end = builtins.min(r_st + ds[i], pad_img_shp[-nd + i])
if not inc_pad:
r_st = builtins.max(r_st, padding[i])
r_end = builtins.min(r_end,
input.shape[-nd + i] + padding[i])
region.append(slice(r_st, r_end))
patch = padded_input[l][region]
output_val[l][r] = func(patch)
return output_val
def perform(self, node, inp, out):
assert self.mode == 'max'
x, maxout, gz = inp
gx_stg, = out
# number of pooling output rows
pr = maxout.shape[-2]
# number of pooling output cols
pc = maxout.shape[-1]
ds0, ds1 = self.ds
st0, st1 = self.st
pad_h = self.padding[0]
pad_w = self.padding[1]
img_rows = x.shape[-2] + 2 * pad_h
img_cols = x.shape[-1] + 2 * pad_w
# pad the image
if self.padding != (0, 0):
y = numpy.zeros((x.shape[0], x.shape[1], img_rows, img_cols),
dtype=x.dtype)
y[:, :, pad_h:(img_rows - pad_h), pad_w:(img_cols - pad_w)] = x
else:
y = x
gx = numpy.zeros_like(y)
for n in range(x.shape[0]):
for k in range(x.shape[1]):
for r in range(pr):
row_st = builtins.max(r * st0, self.padding[0])
row_end = builtins.min(row_st + ds0, img_rows)
for c in range(pc):
col_st = builtins.max(c * st1, self.padding[1])
col_end = builtins.min(col_st + ds1, img_cols)
for row_ind in range(row_st, row_end):
for col_ind in range(col_st, col_end):
if (maxout[n, k, r, c] == y[n, k, row_ind,
col_ind]):
gx[n, k, row_ind, col_ind] += gz[n, k, r,
c]
# unpad the image
gx = gx[:, :, pad_h:(img_rows - pad_h), pad_w:(img_cols - pad_w)]
gx_stg[0] = gx
def perform(self, node, inp, out):
assert self.mode == 'max'
x, maxout, gz, ws, stride, pad = inp
gx_stg, = out
assert ws.shape == stride.shape == pad.shape == (2,)
# number of pooling output rows
pr = maxout.shape[-2]
# number of pooling output cols
pc = maxout.shape[-1]
ws0, ws1 = ws
st0, st1 = stride
pad_h = pad[0]
pad_w = pad[1]
img_rows = x.shape[-2] + 2 * pad_h
img_cols = x.shape[-1] + 2 * pad_w
# pad the image
if (pad_h, pad_w) != (0, 0):
y = numpy.zeros(
(x.shape[0], x.shape[1], img_rows, img_cols),
dtype=x.dtype)
y[:, :, pad_h:(img_rows - pad_h), pad_w:(img_cols - pad_w)] = x
else:
y = x
gx = numpy.zeros_like(y)
for n in xrange(x.shape[0]):
for k in xrange(x.shape[1]):
for r in xrange(pr):
row_st = builtins.max(r * st0, pad_h)
row_end = builtins.min(row_st + ws0, img_rows)
for c in xrange(pc):
col_st = builtins.max(c * st1, pad_w)
col_end = builtins.min(col_st + ws1, img_cols)
for row_ind in xrange(row_st, row_end):
for col_ind in xrange(col_st, col_end):
if (maxout[n, k, r, c] == y[n, k, row_ind, col_ind]):
gx[n, k, row_ind, col_ind] += gz[n, k, r, c]
# unpad the image
gx = gx[:, :, pad_h:(img_rows - pad_h), pad_w:(img_cols - pad_w)]
gx_stg[0] = gx
def numpy_max_pool_nd_stride_padding(
input, ds, ignore_border=True, st=None, padding=None, mode='max'):
assert ignore_border
nd = len(ds)
if padding is None:
padding = (0,) * nd
if st is None:
st = (0,) * nd
assert len(padding) == len(ds) == len(st)
assert all(ds[i] > padding[i] for i in range(nd))
def pad_img(x):
# initialize padded input
y = numpy.zeros(
x.shape[0:-nd] +
tuple(x.shape[-nd + i] + padding[i] * 2 for i in range(nd)),
dtype=x.dtype)
# place the unpadded input in the center
block = ((slice(None),) * (len(x.shape) - nd) +
tuple(slice(padding[i], x.shape[-nd + i] + padding[i])
for i in range(nd)))
y[block] = x
return y
pad_img_shp = list(input.shape[:-nd])
out_shp = list(input.shape[:-nd])
for i in range(nd):
padded_size = input.shape[-nd + i] + 2 * padding[i]
pad_img_shp.append(padded_size)
out_shp.append((padded_size - ds[i]) // st[i] + 1)
output_val = numpy.zeros(out_shp)
padded_input = pad_img(input)
func = numpy.max
if mode == 'sum':
func = numpy.sum
elif mode != 'max':
func = numpy.average
inc_pad = mode == 'average_inc_pad'
for l in numpy.ndindex(*input.shape[:-nd]):
for r in numpy.ndindex(*output_val.shape[-nd:]):
region = []
for i in range(nd):
r_st = r[i] * st[i]
r_end = builtins.min(r_st + ds[i], pad_img_shp[-nd + i])
if not inc_pad:
r_st = builtins.max(r_st, padding[i])
r_end = builtins.min(r_end, input.shape[-nd + i] + padding[i])
region.append(slice(r_st, r_end))
patch = padded_input[l][region]
output_val[l][r] = func(patch)
return output_val
def perform(self, node, inp, out):
assert self.mode == "max"
x, maxout, gz = inp
gx_stg, = out
# number of pooling output rows
pr = maxout.shape[-2]
# number of pooling output cols
pc = maxout.shape[-1]
ds0, ds1 = self.ds
st0, st1 = self.st
pad_h = self.padding[0]
pad_w = self.padding[1]
img_rows = x.shape[-2] + 2 * pad_h
img_cols = x.shape[-1] + 2 * pad_w
# pad the image
if self.padding != (0, 0):
y = numpy.zeros((x.shape[0], x.shape[1], img_rows, img_cols), dtype=x.dtype)
y[:, :, pad_h : (img_rows - pad_h), pad_w : (img_cols - pad_w)] = x
else:
y = x
gx = numpy.zeros_like(y)
for n in xrange(x.shape[0]):
for k in xrange(x.shape[1]):
for r in xrange(pr):
row_st = builtins.max(r * st0, self.padding[0])
row_end = builtins.min(row_st + ds0, img_rows)
for c in xrange(pc):
col_st = builtins.max(c * st1, self.padding[1])
col_end = builtins.min(col_st + ds1, img_cols)
for row_ind in xrange(row_st, row_end):
for col_ind in xrange(col_st, col_end):
if maxout[n, k, r, c] == y[n, k, row_ind, col_ind]:
gx[n, k, row_ind, col_ind] += gz[n, k, r, c]
# unpad the image
gx = gx[:, :, pad_h : (img_rows - pad_h), pad_w : (img_cols - pad_w)]
gx_stg[0] = gx
def perform(self, node, inp, out):
if self.mode not in ('max', 'sum') and self.padding != (0, 0):
raise NotImplementedError()
x, maxout, gz = inp
gx_stg, = out
# number of pooling output rows
pr = maxout.shape[-2]
# number of pooling output cols
pc = maxout.shape[-1]
ds0, ds1 = self.ds
st0, st1 = self.st
pad_h = self.padding[0]
pad_w = self.padding[1]
img_rows = x.shape[-2] + 2 * pad_h
img_cols = x.shape[-1] + 2 * pad_w
inc_pad = self.mode == 'average_inc_pad'
sum_mode = self.mode == 'sum'
# pad the image
if self.padding != (0, 0):
y = numpy.zeros((x.shape[0], x.shape[1], img_rows, img_cols),
dtype=x.dtype)
y[:, :, pad_h:(img_rows - pad_h), pad_w:(img_cols - pad_w)] = x
else:
y = x
gx = numpy.zeros_like(y)
if self.mode == 'max':
for n in xrange(x.shape[0]):
for k in xrange(x.shape[1]):
for r in xrange(pr):
row_st = builtins.max(r * st0, self.padding[0])
row_end = builtins.min(row_st + ds0, img_rows)
for c in xrange(pc):
col_st = builtins.max(c * st1, self.padding[1])
col_end = builtins.min(col_st + ds1, img_cols)
for row_ind in xrange(row_st, row_end):
for col_ind in xrange(col_st, col_end):
if (maxout[n, k, r, c] == y[n, k, row_ind,
col_ind]):
gx[n, k, row_ind, col_ind] += gz[n, k,
r, c]
else:
for n in xrange(x.shape[0]):
for k in xrange(x.shape[1]):
for r in xrange(pr):
if sum_mode or inc_pad:
row_st = r * st0
else:
row_st = builtins.max(r * st0, self.padding[0])
row_end = builtins.min(row_st + ds0, img_rows)
for c in xrange(pc):
if sum_mode or inc_pad:
col_st = c * st1
else:
col_st = builtins.max(c * st1, self.padding[1])
col_end = builtins.min(col_st + ds1, img_cols)
if sum_mode:
val = gz[n, k, r, c]
else:
val = gz[n, k, r, c] / ((row_end - row_st) *
(col_end - col_st))
gx[n, k, row_st:row_end, col_st:col_end] += val
# unpad the image
gx = gx[:, :, pad_h:(img_rows - pad_h), pad_w:(img_cols - pad_w)]
gx_stg[0] = gx
def perform(self, node, inp, out):
if self.mode not in ('max', 'sum') and self.padding != (0, 0):
raise NotImplementedError()
x, maxout, gz = inp
gx_stg, = out
# number of pooling output rows
pr = maxout.shape[-2]
# number of pooling output cols
pc = maxout.shape[-1]
ds0, ds1 = self.ds
st0, st1 = self.st
pad_h = self.padding[0]
pad_w = self.padding[1]
img_rows = x.shape[-2] + 2 * pad_h
img_cols = x.shape[-1] + 2 * pad_w
inc_pad = self.mode == 'average_inc_pad'
sum_mode = self.mode == 'sum'
# pad the image
if self.padding != (0, 0):
y = numpy.zeros(
(x.shape[0], x.shape[1], img_rows, img_cols),
dtype=x.dtype)
y[:, :, pad_h:(img_rows-pad_h), pad_w:(img_cols-pad_w)] = x
else:
y = x
gx = numpy.zeros_like(y)
if self.mode == 'max':
for n in xrange(x.shape[0]):
for k in xrange(x.shape[1]):
for r in xrange(pr):
row_st = builtins.max(r * st0, self.padding[0])
row_end = builtins.min(row_st + ds0, img_rows)
for c in xrange(pc):
col_st = builtins.max(c * st1, self.padding[1])
col_end = builtins.min(col_st + ds1, img_cols)
for row_ind in xrange(row_st, row_end):
for col_ind in xrange(col_st, col_end):
if (maxout[n, k, r, c] == y[n, k, row_ind, col_ind]):
gx[n, k, row_ind, col_ind] += gz[n, k, r, c]
else:
for n in xrange(x.shape[0]):
for k in xrange(x.shape[1]):
for r in xrange(pr):
if sum_mode or inc_pad:
row_st = r * st0
else:
row_st = builtins.max(r * st0, self.padding[0])
row_end = builtins.min(row_st + ds0, img_rows)
for c in xrange(pc):
if sum_mode or inc_pad:
col_st = c * st1
else:
col_st = builtins.max(c * st1,
self.padding[1])
col_end = builtins.min(col_st + ds1, img_cols)
if sum_mode:
val = gz[n, k, r, c]
else:
val = gz[n, k, r, c] / ((row_end - row_st) *
(col_end - col_st))
gx[n, k, row_st:row_end, col_st:col_end] += val
# unpad the image
gx = gx[:, :, pad_h:(img_rows-pad_h), pad_w:(img_cols-pad_w)]
gx_stg[0] = gx
def numpy_pool_2d_stride_padding(
x, ds, ignore_border=True, st=None, padding=(0, 0), mode='max'):
assert (ignore_border is False)
in_h = x.shape[-2]
in_w = x.shape[-1]
kernel_h = ds[0]
kernel_w = ds[1]
stride_h = st[0]
stride_w = st[1]
pad_h = padding[0]
pad_w = padding[1]
assert ds[0] > pad_h
assert ds[1] > pad_w
def pad_img(x):
y = numpy.zeros(
(x.shape[0], x.shape[1],
x.shape[2] + pad_h * 2, x.shape[3] + pad_w * 2),
dtype=x.dtype)
y[:, :, pad_h:(x.shape[2] + pad_h), pad_w:(x.shape[3] + pad_w)] = x
return y
h = in_h + 2 * pad_h
w = in_w + 2 * pad_w
out_h = int(math.ceil((float)(h - kernel_h) / stride_h)) + 1
out_w = int(math.ceil((float)(w - kernel_w) / stride_w)) + 1
out_shp = list(x.shape[:-2])
out_shp.extend([out_h, out_w])
output_val = numpy.zeros(out_shp)
y = pad_img(x)
func = numpy.max
if mode == 'sum':
func = numpy.sum
elif mode != 'max':
func = numpy.average
inc_pad = mode == 'average_inc_pad'
for k in numpy.ndindex(*x.shape[:-2]):
for i in range(output_val.shape[-2]):
ii_st = i * st[0]
if ii_st > h:
print ('ii_st > h!!!')
continue
ii_end = builtins.min(ii_st + ds[0], h)
if not inc_pad:
ii_st = builtins.max(ii_st, pad_h)
ii_end = builtins.min(ii_end, in_h + pad_h)
for j in range(output_val.shape[-1]):
jj_st = j * st[1]
if jj_st > w:
print ('jj_st > w!!!')
continue
jj_end = builtins.min(jj_st + ds[1], w)
if not inc_pad:
jj_st = builtins.max(jj_st, pad_w)
jj_end = builtins.min(jj_end, in_w + pad_w)
patch = y[k][ii_st:ii_end, jj_st:jj_end]
output_val[k][i, j] = func(patch)
return output_val
def numpy_max_pool_2d_stride_pad(x,
ws,
ignore_border=True,
stride=None,
pad=(0, 0),
mode="max"):
assert ignore_border
pad_h = pad[0]
pad_w = pad[1]
h = x.shape[-2]
w = x.shape[-1]
assert ws[0] > pad_h
assert ws[1] > pad_w
def pad_img(x):
y = np.zeros(
(
x.shape[0],
x.shape[1],
x.shape[2] + pad_h * 2,
x.shape[3] + pad_w * 2,
),
dtype=x.dtype,
)
y[:, :, pad_h:(x.shape[2] + pad_h), pad_w:(x.shape[3] + pad_w)] = x
return y
img_rows = h + 2 * pad_h
img_cols = w + 2 * pad_w
out_r = (img_rows - ws[0]) // stride[0] + 1
out_c = (img_cols - ws[1]) // stride[1] + 1
out_shp = list(x.shape[:-2])
out_shp.append(out_r)
out_shp.append(out_c)
ws0, ws1 = ws
stride0, stride1 = stride
output_val = np.zeros(out_shp)
y = pad_img(x)
func = np.max
if mode == "sum":
func = np.sum
elif mode != "max":
func = np.average
inc_pad = mode == "average_inc_pad"
for k in np.ndindex(*x.shape[:-2]):
for i in range(output_val.shape[-2]):
ii_stride = i * stride[0]
ii_end = builtins.min(ii_stride + ws[0], img_rows)
if not inc_pad:
ii_stride = builtins.max(ii_stride, pad_h)
ii_end = builtins.min(ii_end, h + pad_h)
for j in range(output_val.shape[-1]):
jj_stride = j * stride[1]
jj_end = builtins.min(jj_stride + ws[1], img_cols)
if not inc_pad:
jj_stride = builtins.max(jj_stride, pad_w)
jj_end = builtins.min(jj_end, w + pad_w)
patch = y[k][ii_stride:ii_end, jj_stride:jj_end]
output_val[k][i, j] = func(patch)
return output_val
