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提交 c58953f6 authored 作者: amrithasuresh's avatar amrithasuresh
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Updated numpy as np

上级 da609fe9
隐藏空白字符变更
内嵌 并排
正在显示 包含 29 行增加 和 29 行删除
theano/tensor/signal/pool.py
浏览文件 @ c58953f6
......@@ -9,7 +9,7 @@ from __future__ import absolute_import, print_function, division
import warnings
import itertools
import numpy
import numpy as np
from six.moves import xrange
import six.moves.builtins as builtins
import theano
......@@ -412,7 +412,7 @@ class Pool(OpenMPOp):
if isinstance(out, theano.Variable):
return tensor.maximum(out, 0)
else:
return numpy.maximum(out, 0)
return np.maximum(out, 0)
else:
if isinstance(v, theano.Variable):
return tensor.switch(tensor.ge(stride, downsample),
......@@ -516,7 +516,7 @@ class Pool(OpenMPOp):
if not self.ignore_border:
assert all(z > 0 for z in z_shape[-nd:])
if (z[0] is None) or (z[0].shape != z_shape):
z[0] = numpy.empty(z_shape, dtype=x.dtype)
z[0] = np.empty(z_shape, dtype=x.dtype)
zz = z[0]
# size of pooling output
pool_out_shp = zz.shape[-nd:]
......@@ -525,16 +525,16 @@ class Pool(OpenMPOp):
# pad the image
if max(pad) != 0:
y = numpy.zeros(x.shape[:-nd] + img_shp, dtype=x.dtype)
y = np.zeros(x.shape[:-nd] + img_shp, dtype=x.dtype)
y[(slice(None),) * (len(x.shape) - nd) +
tuple(slice(pad[i], img_shp[i] - pad[i]) for i in xrange(nd))] = x
else:
y = x
func = numpy.max
func = np.max
if self.mode == 'sum':
func = numpy.sum
func = np.sum
elif self.mode != 'max':
func = numpy.average
func = np.average
# precompute the region boundaries for each dimension
region_slices = [[] for i in xrange(nd)]
......@@ -548,11 +548,11 @@ class Pool(OpenMPOp):
region_slices[i].append(slice(start, end))
# iterate over non-pooling dimensions
for k in numpy.ndindex(*x.shape[:-nd]):
for k in np.ndindex(*x.shape[:-nd]):
zzk = zz[k]
yk = y[k]
# iterate over pooling regions
for r in numpy.ndindex(*pool_out_shp):
for r in np.ndindex(*pool_out_shp):
zzk[r] = func(
yk[[region_slices[i][r[i]] for i in xrange(nd)]])
......@@ -1020,7 +1020,7 @@ class PoolGrad(OpenMPOp):
if isinstance(out, theano.Variable):
return tensor.maximum(out, 0)
else:
return numpy.maximum(out, 0)
return np.maximum(out, 0)
else:
if isinstance(v, theano.Variable):
return tensor.switch(tensor.ge(stride, downsample),
......@@ -1128,12 +1128,12 @@ class MaxPoolGrad(PoolGrad):
# pad the image
if max(pad) != 0:
y = numpy.zeros(x.shape[:-nd] + img_shp, dtype=x.dtype)
y = np.zeros(x.shape[:-nd] + img_shp, dtype=x.dtype)
y[(slice(None),) * (len(x.shape) - nd) +
tuple(slice(pad[i], img_shp[i] - pad[i]) for i in xrange(nd))] = x
else:
y = x
gx = numpy.zeros_like(y)
gx = np.zeros_like(y)
# precompute the region boundaries for each dimension
region_ranges = [[] for i in xrange(nd)]
......@@ -1144,13 +1144,13 @@ class MaxPoolGrad(PoolGrad):
region_ranges[i].append(xrange(start, end))
# iterate over non-pooling dimensions
for k in numpy.ndindex(*x.shape[:-nd]):
for k in np.ndindex(*x.shape[:-nd]):
gxk = gx[k]
gzk = gz[k]
yk = y[k]
maxoutk = maxout[k]
# iterate over pooling regions
for r in numpy.ndindex(*pool_out_shp):
for r in np.ndindex(*pool_out_shp):
maxout_value = maxoutk[r]
# iterate inside region
for c in itertools.product(*[region_ranges[i][r[i]]
......@@ -1444,7 +1444,7 @@ class AveragePoolGrad(PoolGrad):
raise NotImplementedError()
z_shape = self.out_shape(x.shape, ws, self.ignore_border, stride, pad, nd)
if (gx_stg[0] is None) or (gx_stg[0].shape != z_shape):
gx_stg[0] = numpy.empty(z_shape, dtype=x.dtype)
gx_stg[0] = np.empty(z_shape, dtype=x.dtype)
zz = gx_stg[0]
# size of pooling output
pool_out_shp = zz.shape[-nd:]
......@@ -1453,7 +1453,7 @@ class AveragePoolGrad(PoolGrad):
sum_mode = self.mode == 'sum'
# initialize the padded output
gx = numpy.zeros((x.shape[:-nd] + img_shp), dtype=x.dtype)
gx = np.zeros((x.shape[:-nd] + img_shp), dtype=x.dtype)
# precompute the region boundaries and sizes for each dimension
region_slices = [[] for i in xrange(nd)]
......@@ -1470,11 +1470,11 @@ class AveragePoolGrad(PoolGrad):
# iterate over non-pooling dimensions
region_slice = [None] * nd
for k in numpy.ndindex(*x.shape[:-nd]):
for k in np.ndindex(*x.shape[:-nd]):
gzk = gz[k]
gxk = gx[k]
# iterate over pooling regions
for r in numpy.ndindex(*pool_out_shp):
for r in np.ndindex(*pool_out_shp):
region_size = 1
for i in xrange(nd):
region_slice[i] = region_slices[i][r[i]]
......@@ -1783,7 +1783,7 @@ class DownsampleFactorMaxGradGrad(OpenMPOp):
'DownsampleFactorMaxGradGrad requires input '
'with {} or more dimensions'.format(nd))
if (z[0] is None) or (z[0].shape != maxout.shape):
z[0] = numpy.zeros(maxout.shape, dtype=x.dtype)
z[0] = np.zeros(maxout.shape, dtype=x.dtype)
ggz = z[0] # grad wrt maxout_grad has the same shape as maxout
# size of pooling output
pool_out_shp = ggz.shape[-nd:]
......@@ -1791,10 +1791,10 @@ class DownsampleFactorMaxGradGrad(OpenMPOp):
# pad the image and its gradients
if max(pad) > 0:
y_padded = numpy.zeros(x.shape[:-nd] + img_shp, dtype=x.dtype)
y_padded = np.zeros(x.shape[:-nd] + img_shp, dtype=x.dtype)
y_padded[(slice(None),) * (len(x.shape) - nd) +
tuple(slice(pad[i], img_shp[i] - pad[i]) for i in xrange(nd))] = x
ggx_padded = numpy.zeros(x.shape[:-nd] + img_shp, dtype=x.dtype)
ggx_padded = np.zeros(x.shape[:-nd] + img_shp, dtype=x.dtype)
ggx_padded[(slice(None),) * (len(x.shape) - nd) +
tuple(slice(pad[i], img_shp[i] - pad[i]) for i in xrange(nd))] = ggx
......@@ -1811,13 +1811,13 @@ class DownsampleFactorMaxGradGrad(OpenMPOp):
region_ranges[i].append(xrange(start, end))
# iterate over non-pooling dimensions
for k in numpy.ndindex(*x.shape[:-nd]):
for k in np.ndindex(*x.shape[:-nd]):
ggxk = ggx_padded[k]
ggzk = ggz[k]
yk = y_padded[k]
maxoutk = maxout[k]
# iterate over pooling regions
for r in numpy.ndindex(*pool_out_shp):
for r in np.ndindex(*pool_out_shp):
# iterate inside region
maxout_value = maxoutk[r]
for c in itertools.product(*[region_ranges[i][r[i]]
......@@ -2113,7 +2113,7 @@ class MaxPoolRop(OpenMPOp):
if not self.ignore_border:
assert all(z > 0 for z in z_shape[-nd:])
if (z[0] is None) or (z[0].shape != z_shape):
z[0] = numpy.empty(z_shape, dtype=x.dtype)
z[0] = np.empty(z_shape, dtype=x.dtype)
zz = z[0]
# size of pooling output
pool_out_shp = zz.shape[-nd:]
......@@ -2122,10 +2122,10 @@ class MaxPoolRop(OpenMPOp):
# pad the image and the eval point
if max(pad) != 0:
y = numpy.zeros(x.shape[:-nd] + img_shp, dtype=x.dtype)
y = np.zeros(x.shape[:-nd] + img_shp, dtype=x.dtype)
y[(slice(None),) * (len(x.shape) - nd) +
tuple(slice(pad[i], img_shp[i] - pad[i]) for i in xrange(nd))] = x
ey = numpy.zeros(ex.shape[:-nd] + img_shp, dtype=ex.dtype)
ey = np.zeros(ex.shape[:-nd] + img_shp, dtype=ex.dtype)
ey[(slice(None),) * (len(ex.shape) - nd) +
tuple(slice(pad[i], img_shp[i] - pad[i]) for i in xrange(nd))] = ex
else:
......@@ -2144,18 +2144,18 @@ class MaxPoolRop(OpenMPOp):
region_slices[i].append(slice(start, end))
# iterate over non-pooling dimensions
for k in numpy.ndindex(*x.shape[:-nd]):
for k in np.ndindex(*x.shape[:-nd]):
zzk = zz[k]
yk = y[k]
eyk = ey[k]
# iterate over pooling regions
for r in numpy.ndindex(*pool_out_shp):
for r in np.ndindex(*pool_out_shp):
# current slice in padded input
ykslice = yk[[region_slices[i][r[i]] for i in xrange(nd)]]
# current slice in eval points
eykslice = eyk[[region_slices[i][r[i]] for i in xrange(nd)]]
# indices of maximum
idx = numpy.unravel_index(numpy.argmax(ykslice), ykslice.shape)
idx = np.unravel_index(np.argmax(ykslice), ykslice.shape)
zzk[r] = eykslice[idx]
def c_headers(self):
......
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