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提交 be3fee10 authored 作者: Olivier Delalleau's avatar Olivier Delalleau
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Merge pull request #225 from nouiz/fix_neibs

Fix neibs
上级 f7f5c1a6 34bb1a3d
隐藏空白字符变更
内嵌 并排
正在显示 包含 250 行增加 和 217 行删除
theano/sandbox/neighbours.py
浏览文件 @ be3fee10
import theano
from theano import Op, Apply
import theano.tensor as T
from theano.tensor.opt import register_specialize
from theano.gof import local_optimizer
from theano.sandbox.cuda import cuda_available
......@@ -10,6 +9,13 @@ if cuda_available:
from theano.sandbox.cuda.basic_ops import host_from_gpu, gpu_from_host
from theano.sandbox.cuda.opt import register_opt as register_gpu_opt
class BadOldCode(Exception):
""" We create a specific Exception to be sure it don't get caught
by mistake"""
pass
class Images2Neibs(Op):
def __init__(self, mode='valid'):
"""
......@@ -20,26 +26,32 @@ class Images2Neibs(Op):
is not a multiple of the pooling factor(s)
wrap_centered : ?? TODO comment
"""
if mode not in ['valid','wrap_centered','ignore_borders']:
raise NotImplementedError("Only the mode valid, ignore_borders and wrap_centered have been implemented for the op Images2Neibs")
if mode not in ['valid', 'wrap_centered', 'ignore_borders']:
raise NotImplementedError("Only the mode valid, ignore_borders"
" and wrap_centered have been"
" implemented for the op Images2Neibs")
self.mode = mode
def __eq__(self, other):
return type(self) == type(other) and self.mode==other.mode
return type(self) == type(other) and self.mode == other.mode
def __hash__(self):
return hash(type(self))^hash(self.mode)
return hash(type(self)) ^ hash(self.mode)
def __str__(self):
return self.__class__.__name__+"{%s}"%self.mode
return self.__class__.__name__ + "{%s}" % self.mode
def __setstate__(self, d):
self.__dict__.update(d)
if not hasattr(self,"mode"):
if not hasattr(self, "mode"):
self.mode = 'valid'
def make_node(self, ten4, neib_shape, neib_step=None):
"""
:param neib_step: (dx,dy) where dx is the number of rows to skip between patch
and dy is the number of columns. When None, this is the same
as neib_shape(patch are disjoint)
:param neib_step: (dx,dy) where dx is the number of rows to
skip between patch and dy is the number of
columns. When None, this is the same as
neib_shape(patch are disjoint)
"""
ten4 = T.as_tensor_variable(ten4)
neib_shape = T.as_tensor_variable(neib_shape)
......@@ -48,17 +60,23 @@ class Images2Neibs(Op):
else:
neib_step = T.as_tensor_variable(neib_step)
assert ten4.ndim==4
assert neib_shape.ndim==1
assert neib_step.ndim==1
assert ten4.ndim == 4
assert neib_shape.ndim == 1
assert neib_step.ndim == 1
return Apply(self, [ten4, neib_shape,neib_step], [T.matrix(dtype=ten4.type.dtype)])
return Apply(self, [ten4, neib_shape, neib_step],
[T.matrix(dtype=ten4.type.dtype)])
def grad(self, inp, grads):
x, neib_shape, neib_step = inp
gz, = grads
if self.mode in ['valid','ignore_borders']:
return [neibs2images(gz, neib_shape, x.shape, mode=self.mode), None, None]
if self.mode in ['valid', 'ignore_borders']:
raise BadOldCode("The Images2Neibs grad is not implemented."
" It was in the past, but returned the wrong"
" answer!")
# This is the reverse of the op, not the grad!
return [neibs2images(gz, neib_shape, x.shape, mode=self.mode),
None, None]
else:
raise NotImplementedError()
......@@ -70,7 +88,7 @@ class Images2Neibs(Op):
z, = out
fail = sub['fail']
mode=self.mode
mode = self.mode
return """
int grid_c = -1; //number of patch in height
int grid_d = -1; //number of patch in width
......@@ -87,7 +105,8 @@ class Images2Neibs(Op):
}
if ( (%(neib_shape)s->dimensions)[0] != 2)
{
PyErr_Format(PyExc_TypeError, "neib_shape wrong shape ; has to contain 2 elements");
PyErr_Format(PyExc_TypeError, "neib_shape wrong shape ; has to"
" contain 2 elements");
%(fail)s;
}
if (%(neib_step)s->nd != 1)
......@@ -97,7 +116,8 @@ class Images2Neibs(Op):
}
if ( (%(neib_step)s->dimensions)[0] != 2)
{
PyErr_Format(PyExc_TypeError, "neib_step wrong step ; has to contain 2 elements");
PyErr_Format(PyExc_TypeError,
"neib_step wrong step ; has to contain 2 elements");
%(fail)s;
}
......@@ -229,9 +249,11 @@ class Images2Neibs(Op):
} // END NESTED SCOPE
""" % locals()
def images2neibs(ten4, neib_shape, neib_step=None, mode='valid'):
return Images2Neibs(mode)(ten4, neib_shape, neib_step)
def neibs2images(neibs, neib_shape, original_shape, mode='valid'):
"""
Inverse of images2neib.
......@@ -246,19 +268,21 @@ def neibs2images(neibs, neib_shape, original_shape, mode='valid'):
neib_shape = T.as_tensor_variable(neib_shape)
original_shape = T.as_tensor_variable(original_shape)
new_neib_shape = T.stack(original_shape[-1] // neib_shape[1], neib_shape[1])
output_2d = images2neibs(neibs.dimshuffle('x','x',0,1), new_neib_shape, mode=mode)
new_neib_shape = T.stack(original_shape[-1] // neib_shape[1],
neib_shape[1])
output_2d = images2neibs(neibs.dimshuffle('x', 'x', 0, 1),
new_neib_shape, mode=mode)
if mode == 'ignore_borders':
valid_shape = list(original_shape)
valid_shape[2] = (valid_shape[2] // neib_shape[0]) * neib_shape[0]
valid_shape[3] = (valid_shape[3] // neib_shape[1]) * neib_shape[1]
valid_shape[2] = (valid_shape[2] // neib_shape[0]) * neib_shape[0]
valid_shape[3] = (valid_shape[3] // neib_shape[1]) * neib_shape[1]
output_4d = output_2d.reshape(valid_shape)
#padding the borders with zeros
for d in [2,3]:
for d in [2, 3]:
pad_shape = list(output_4d.shape)
pad_shape[d] = original_shape[d] - valid_shape[d]
output_4d = T.concatenate([output_4d,T.zeros(pad_shape)],axis=d)
output_4d = T.concatenate([output_4d, T.zeros(pad_shape)], axis=d)
else:
output_4d = output_2d.reshape(original_shape)
......@@ -269,7 +293,9 @@ def neibs2images(neibs, neib_shape, original_shape, mode='valid'):
class GpuImages2Neibs(Images2Neibs):
def __init__(self, mode='valid'):
if mode not in ['valid', 'wrap_centered']:
raise NotImplementedError("Only the mode valid and wrap_centered have been implemented for the op GpuImages2Neibs")
raise NotImplementedError("Only the mode valid and wrap_centered"
" have been implemented for the op"
" GpuImages2Neibs")
self.mode = mode
def make_node(self, ten4, neib_shape, neib_step):
......@@ -277,12 +303,13 @@ class GpuImages2Neibs(Images2Neibs):
if not isinstance(ten4.type, CudaNdarrayType):
raise TypeError('ten4 must be cudandarray', ten4)
assert ten4.ndim==4
assert neib_shape.ndim==1
assert neib_step.ndim==1
assert ten4.ndim == 4
assert neib_shape.ndim == 1
assert neib_step.ndim == 1
return Apply(self, [ten4, neib_shape, neib_step], [CudaNdarrayType(broadcastable=(False,False),
dtype=ten4.type.dtype)()])
return Apply(self, [ten4, neib_shape, neib_step],
[CudaNdarrayType(broadcastable=(False, False),
dtype=ten4.type.dtype)()])
def c_code_cache_version(self):
return (7,)
......@@ -502,7 +529,8 @@ class GpuImages2Neibs(Images2Neibs):
%(z)s = (CudaNdarray*)CudaNdarray_NewDims(2, dims);
if (!%(z)s)
{
PyErr_SetString(PyExc_MemoryError, "failed to alloc z output");
PyErr_SetString(PyExc_MemoryError,
"failed to alloc z output");
%(fail)s;
}
}
......@@ -567,7 +595,9 @@ class GpuImages2Neibs(Images2Neibs):
cudaError_t sts = cudaGetLastError();
if (cudaSuccess != sts)
{
PyErr_Format(PyExc_RuntimeError, "Cuda error: %%s: %%s. (grid: %%i x %%i; block: %%i x %%i x %%i; shared: %%i)\\n",
PyErr_Format(PyExc_RuntimeError,
"Cuda error: %%s: %%s. (grid: %%i x %%i;"
" block: %%i x %%i x %%i; shared: %%i)\\n",
"k_multi_warp_%(name)s",
cudaGetErrorString(sts),
n_blocks.x,
......@@ -581,13 +611,18 @@ class GpuImages2Neibs(Images2Neibs):
} // END NESTED SCOPE
""" % locals()
def gpu_images2neibs(ten4, neib_shape, neib_step=None, mode='valid'):
return GpuImages2Neibs(mode)(ten4, neib_shape, neib_step)
@local_optimizer()
def use_gpu_images2neibs(node):
if type(node.op) is Images2Neibs:
return [host_from_gpu(gpu_images2neibs(gpu_from_host(node.inputs[0]),node.inputs[1],node.inputs[2],mode=node.op.mode))]
return [host_from_gpu(gpu_images2neibs(gpu_from_host(node.inputs[0]),
node.inputs[1], node.inputs[2],
mode=node.op.mode))]
if cuda_available:
register_gpu_opt()(use_gpu_images2neibs)
theano/sandbox/test_neighbours.py
浏览文件 @ be3fee10
import numpy
import numpy.random
import theano
from theano import shared, function
import theano.tensor as T
from neighbours import images2neibs, neibs2images, Images2Neibs, GpuImages2Neibs
from neighbours import (images2neibs, neibs2images,
Images2Neibs, GpuImages2Neibs)
# Skip test if cuda_ndarray is not available.
from nose.plugins.skip import SkipTest
import theano.sandbox.cuda as cuda
from theano.tests import unittest_tools
if theano.config.mode=='FAST_COMPILE':
if theano.config.mode == 'FAST_COMPILE':
mode_with_gpu = theano.compile.mode.get_mode('FAST_RUN').including('gpu')
mode_without_gpu = theano.compile.mode.get_mode('FAST_RUN').excluding('gpu')
mode_without_gpu = theano.compile.mode.get_mode(
'FAST_RUN').excluding('gpu')
else:
mode_with_gpu = theano.compile.mode.get_default_mode().including('gpu')
mode_without_gpu = theano.compile.mode.get_default_mode().excluding('gpu')
def test_neibs():
shape = (100,40,18,18)
shape = (100, 40, 18, 18)
images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
neib_shape = T.as_tensor_variable((2,2))#(array((2,2), dtype='float32'))
neib_shape = T.as_tensor_variable((2, 2))
f = function([], images2neibs(images, neib_shape), mode=mode_without_gpu)
#print images.get_value(borrow=True)
neibs = f()
#print neibs
g = function([], neibs2images(neibs, neib_shape, images.shape), mode=mode_without_gpu)
g = function([], neibs2images(neibs, neib_shape, images.shape),
mode=mode_without_gpu)
#print g()
assert numpy.allclose(images.get_value(borrow=True),g())
assert numpy.allclose(images.get_value(borrow=True), g())
def test_neibs_bad_shape():
shape = (2,3,10,10)
shape = (2, 3, 10, 10)
images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
neib_shape = T.as_tensor_variable((3,2))
neib_shape = T.as_tensor_variable((3, 2))
try:
f = function([], images2neibs(images, neib_shape), mode=mode_without_gpu)
f = function([], images2neibs(images, neib_shape),
mode=mode_without_gpu)
neibs = f()
#print neibs
assert False,"An error was expected"
assert False, "An error was expected"
except TypeError:
pass
shape = (2,3,10,10)
shape = (2, 3, 10, 10)
images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
neib_shape = T.as_tensor_variable((2,3))
neib_shape = T.as_tensor_variable((2, 3))
try:
f = function([], images2neibs(images, neib_shape), mode=mode_without_gpu)
f = function([], images2neibs(images, neib_shape),
mode=mode_without_gpu)
neibs = f()
#print neibs
assert False,"An error was expected"
assert False, "An error was expected"
except TypeError:
pass
def test_neibs_bad_shape_warp_centered():
shape = (2,3,10,10)
shape = (2, 3, 10, 10)
images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
neib_shape = T.as_tensor_variable((3,2))
neib_shape = T.as_tensor_variable((3, 2))
try:
f = function([], images2neibs(images, neib_shape, mode="wrap_centered"), mode=mode_without_gpu)
f = function([], images2neibs(images, neib_shape,
mode="wrap_centered"),
mode=mode_without_gpu)
neibs = f()
#print neibs
assert False,"An error was expected"
assert False, "An error was expected"
except TypeError:
pass
shape = (2,3,10,10)
shape = (2, 3, 10, 10)
images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
neib_shape = T.as_tensor_variable((2,3))
neib_shape = T.as_tensor_variable((2, 3))
try:
f = function([], images2neibs(images, neib_shape, mode="wrap_centered"), mode=mode_without_gpu)
f = function([], images2neibs(images, neib_shape,
mode="wrap_centered"),
mode=mode_without_gpu)
neibs = f()
#print neibs
assert False,"An error was expected"
assert False, "An error was expected"
except TypeError:
pass
shape = (2,3,2,3)
shape = (2, 3, 2, 3)
images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
neib_shape = T.as_tensor_variable((3,3))
neib_shape = T.as_tensor_variable((3, 3))
try:
f = function([], images2neibs(images, neib_shape, mode="wrap_centered"), mode=mode_without_gpu)
f = function([], images2neibs(images, neib_shape,
mode="wrap_centered"),
mode=mode_without_gpu)
neibs = f()
#print neibs
assert False,"An error was expected"
assert False, "An error was expected"
except TypeError:
pass
shape = (2,3,3,2)
shape = (2, 3, 3, 2)
images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
neib_shape = T.as_tensor_variable((3,3))
neib_shape = T.as_tensor_variable((3, 3))
try:
f = function([], images2neibs(images, neib_shape, mode="wrap_centered"), mode=mode_without_gpu)
f = function([], images2neibs(images, neib_shape,
mode="wrap_centered"),
mode=mode_without_gpu)
neibs = f()
#print neibs
assert False,"An error was expected"
except TypeError,e:
assert False, "An error was expected"
except TypeError, e:
pass
shape = (2,3,3,3)
shape = (2, 3, 3, 3)
images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
neib_shape = T.as_tensor_variable((3,3))
neib_shape = T.as_tensor_variable((3, 3))
f = function([], images2neibs(images, neib_shape, mode="wrap_centered"), mode=mode_without_gpu)
f = function([], images2neibs(images, neib_shape, mode="wrap_centered"),
mode=mode_without_gpu)
neibs = f()
#print neibs
def test_neibs_manual():
shape = (2,3,4,4)
shape = (2, 3, 4, 4)
images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
neib_shape = T.as_tensor_variable((2,2))
neib_shape = T.as_tensor_variable((2, 2))
f = function([], images2neibs(images, neib_shape), mode=mode_without_gpu)
......@@ -148,29 +165,34 @@ def test_neibs_manual():
[82, 83, 86, 87],
[88, 89, 92, 93],
[90, 91, 94, 95]])
g = function([], neibs2images(neibs, neib_shape, images.shape), mode=mode_without_gpu)
g = function([], neibs2images(neibs, neib_shape, images.shape),
mode=mode_without_gpu)
#print g()
assert numpy.allclose(images.get_value(borrow=True), g())
def test_neibs_step_manual():
shape = (2,3,5,5)
images = shared(numpy.asarray(numpy.arange(numpy.prod(shape)).reshape(shape),dtype='float32'))
neib_shape = T.as_tensor_variable((3,3))
neib_step = T.as_tensor_variable((2,2))
shape = (2, 3, 5, 5)
images = shared(numpy.asarray(numpy.arange(numpy.prod(
shape)).reshape(shape), dtype='float32'))
neib_shape = T.as_tensor_variable((3, 3))
neib_step = T.as_tensor_variable((2, 2))
modes = [mode_without_gpu]
if cuda.cuda_available:
modes.append(mode_with_gpu)
for mode_idx,mode in enumerate(modes):
f = function([], images2neibs(images, neib_shape, neib_step), mode=mode)
for mode_idx, mode in enumerate(modes):
f = function([], images2neibs(images, neib_shape, neib_step),
mode=mode)
#print images.get_value(borrow=True)
neibs = f()
if mode_idx==0:
assert Images2Neibs in [type(node.op) for node in f.maker.env.toposort()]
elif mode_idx==1:
assert GpuImages2Neibs in [type(node.op) for node in f.maker.env.toposort()]
if mode_idx == 0:
assert Images2Neibs in [type(node.op)
for node in f.maker.env.toposort()]
elif mode_idx == 1:
assert GpuImages2Neibs in [type(node.op)
for node in f.maker.env.toposort()]
assert numpy.allclose(neibs,
[[ 0, 1, 2, 5, 6, 7, 10, 11, 12],
......@@ -202,6 +224,7 @@ def test_neibs_step_manual():
#print g()
#assert numpy.allclose(images.get_value(borrow=True),g())
def test_neibs_wrap_centered_step_manual():
modes = [mode_without_gpu]
......@@ -221,57 +244,63 @@ def test_neibs_wrap_centered_step_manual():
[ 22, 23, 24, 2, 3, 4, 7, 8, 9],
[ 14, 10, 11, 19, 15, 16, 24, 20, 21],
[ 12, 13, 14, 17, 18, 19, 22, 23, 24]]
expected3 = [[ 19, 15, 16, 24, 20, 21, 4, 0, 1, 9, 5, 6, 14, 10, 11],
[ 17, 18, 19, 22, 23, 24, 2, 3, 4, 7, 8, 9, 12, 13, 14],
[ 9, 5, 6, 14, 10, 11, 19, 15, 16, 24, 20, 21, 4, 0, 1],
[ 7, 8, 9, 12, 13, 14, 17, 18, 19, 22, 23, 24, 2, 3, 4]]
expected4 = [[ 23, 24, 20, 21, 22, 3, 4, 0, 1, 2, 8, 9, 5, 6, 7],
[ 21, 22, 23, 24, 20, 1, 2, 3, 4, 0, 6, 7, 8, 9, 5],
[ 13, 14, 10, 11, 12, 18, 19, 15, 16, 17, 23, 24, 20, 21, 22],
[ 11, 12, 13, 14, 10, 16, 17, 18, 19, 15, 21, 22, 23, 24, 20]]
expected5 = [[ 24, 20, 21, 4, 0, 1, 9, 5, 6],
[ 22, 23, 24, 2, 3, 4, 7, 8, 9],
[ 9, 5, 6, 14, 10, 11, 19, 15, 16],
[ 7, 8, 9, 12, 13, 14, 17, 18, 19],
[ 19, 15, 16, 24, 20, 21, 4, 0, 1],
[ 17, 18, 19, 22, 23, 24, 2, 3, 4]]
expected6 = [[ 24, 20, 21, 4, 0, 1, 9, 5, 6],
[ 21, 22, 23, 1, 2, 3, 6, 7, 8],
[ 23, 24, 20, 3, 4, 0, 8, 9, 5],
[ 14, 10, 11, 19, 15, 16, 24, 20, 21],
[ 11, 12, 13, 16, 17, 18, 21, 22, 23],
[ 13, 14, 10, 18, 19, 15, 23, 24, 20]]
expected3 = [[19, 15, 16, 24, 20, 21, 4, 0, 1, 9, 5, 6, 14, 10, 11],
[17, 18, 19, 22, 23, 24, 2, 3, 4, 7, 8, 9, 12, 13, 14],
[ 9, 5, 6, 14, 10, 11, 19, 15, 16, 24, 20, 21, 4, 0, 1],
[ 7, 8, 9, 12, 13, 14, 17, 18, 19, 22, 23, 24, 2, 3, 4]]
expected4 = [[23, 24, 20, 21, 22, 3, 4, 0, 1, 2, 8, 9, 5, 6, 7],
[21, 22, 23, 24, 20, 1, 2, 3, 4, 0, 6, 7, 8, 9, 5],
[13, 14, 10, 11, 12, 18, 19, 15, 16, 17, 23, 24, 20, 21, 22],
[11, 12, 13, 14, 10, 16, 17, 18, 19, 15, 21, 22, 23, 24, 20]]
expected5 = [[24, 20, 21, 4, 0, 1, 9, 5, 6],
[22, 23, 24, 2, 3, 4, 7, 8, 9],
[ 9, 5, 6, 14, 10, 11, 19, 15, 16],
[ 7, 8, 9, 12, 13, 14, 17, 18, 19],
[19, 15, 16, 24, 20, 21, 4, 0, 1],
[17, 18, 19, 22, 23, 24, 2, 3, 4]]
expected6 = [[24, 20, 21, 4, 0, 1, 9, 5, 6],
[21, 22, 23, 1, 2, 3, 6, 7, 8],
[23, 24, 20, 3, 4, 0, 8, 9, 5],
[14, 10, 11, 19, 15, 16, 24, 20, 21],
[11, 12, 13, 16, 17, 18, 21, 22, 23],
[13, 14, 10, 18, 19, 15, 23, 24, 20]]
#TODO test discontinous image
for shp_idx,(shape,neib_shape,neib_step,expected) in enumerate([
[(7,8,5,5),(3,3),(2,2),expected1],
[(7,8,5,5),(3,3),(3,3),expected2],
[(7,8,5,5),(5,3),(3,3),expected3],
[(7,8,5,5),(3,5),(3,3),expected4],
[(80,90,5,5),(3,3),(2,3),expected5],
[(1025,9,5,5),(3,3),(3,2),expected6],
[(1,1,5,1035),(3,3),(3,3),None],
[(1,1,1045,5),(3,3),(3,3),None],
for shp_idx, (shape, neib_shape, neib_step, expected) in enumerate([
[(7, 8, 5, 5), (3, 3), (2, 2), expected1],
[(7, 8, 5, 5), (3, 3), (3, 3), expected2],
[(7, 8, 5, 5), (5, 3), (3, 3), expected3],
[(7, 8, 5, 5), (3, 5), (3, 3), expected4],
[(80, 90, 5, 5), (3, 3), (2, 3), expected5],
[(1025, 9, 5, 5), (3, 3), (3, 2), expected6],
[(1, 1, 5, 1035), (3, 3), (3, 3), None],
[(1, 1, 1045, 5), (3, 3), (3, 3), None],
]):
images = shared(numpy.asarray(numpy.arange(numpy.prod(shape)).reshape(shape),dtype='float32'))
images = shared(numpy.asarray(numpy.arange(numpy.prod(
shape)).reshape(shape), dtype='float32'))
neib_shape = T.as_tensor_variable(neib_shape)
neib_step = T.as_tensor_variable(neib_step)
expected = numpy.asarray(expected)
for mode_idx,mode in enumerate(modes):
f = function([], images2neibs(images, neib_shape, neib_step, mode="wrap_centered"), mode=mode)
for mode_idx, mode in enumerate(modes):
f = function([], images2neibs(images, neib_shape, neib_step,
mode="wrap_centered"), mode=mode)
neibs = f()
if expected.size>1:
for i in range(shape[0]*shape[1]):
assert numpy.allclose(neibs[i*expected.shape[0]:(i+1)*expected.shape[0],:],expected+25*i), mode_idx
if expected.size > 1:
for i in range(shape[0] * shape[1]):
assert numpy.allclose(neibs[i * expected.shape[0]:
(i + 1) * expected.shape[0], :],
expected + 25 * i), mode_idx
if mode_idx==0:
assert Images2Neibs in [type(node.op) for node in f.maker.env.toposort()]
elif mode_idx==1:
assert GpuImages2Neibs in [type(node.op) for node in f.maker.env.toposort()]
if mode_idx == 0:
assert Images2Neibs in [type(node.op)
for node in f.maker.env.toposort()]
elif mode_idx == 1:
assert GpuImages2Neibs in [type(node.op)
for node in f.maker.env.toposort()]
#g = function([], neibs2images(neibs, neib_shape, images.shape), mode=mode_without_gpu)
......@@ -281,123 +310,82 @@ def test_neibs_wrap_centered_step_manual():
def test_neibs_gpu():
if cuda.cuda_available == False:
raise SkipTest('Optional package cuda disabled')
for shape, pshape in [((100,40,18,18),(2,2)),
((100,40,6,18),(3,2)),
((10,40,66,66),(33,33)),
((10,40,68,66),(34,33))
for shape, pshape in [((100, 40, 18, 18), (2, 2)),
((100, 40, 6, 18), (3, 2)),
((10, 40, 66, 66), (33, 33)),
((10, 40, 68, 66), (34, 33))
]:
images = shared(numpy.arange(numpy.prod(shape), dtype='float32').reshape(shape))
images = shared(numpy.arange(numpy.prod(shape),
dtype='float32').reshape(shape))
neib_shape = T.as_tensor_variable(pshape)
from theano.sandbox.cuda.basic_ops import gpu_from_host
f = function([], images2neibs(images,neib_shape),
f = function([], images2neibs(images, neib_shape),
mode=mode_with_gpu)
f_gpu = function([], images2neibs(images,neib_shape),
f_gpu = function([], images2neibs(images, neib_shape),
mode=mode_with_gpu)
assert any([isinstance(node.op,GpuImages2Neibs) for node in f_gpu.maker.env.toposort()])
assert any([isinstance(node.op, GpuImages2Neibs)
for node in f_gpu.maker.env.toposort()])
#print images.get_value(borrow=True)
neibs = numpy.asarray(f_gpu())
assert numpy.allclose(neibs,f())
assert numpy.allclose(neibs, f())
#print neibs
g = function([], neibs2images(neibs, neib_shape, images.shape), mode=mode_with_gpu)
assert any([isinstance(node.op,GpuImages2Neibs) for node in f.maker.env.toposort()])
g = function([], neibs2images(neibs, neib_shape, images.shape),
mode=mode_with_gpu)
assert any([isinstance(node.op, GpuImages2Neibs)
for node in f.maker.env.toposort()])
#print numpy.asarray(g())
assert numpy.allclose(images.get_value(borrow=True), g())
def speed_neibs():
shape = (100,40,18,18)
images = shared(numpy.arange(numpy.prod(shape), dtype='float32').reshape(shape))
neib_shape = T.as_tensor_variable((3,3))
from theano.sandbox.cuda.basic_ops import gpu_from_host
shape = (100, 40, 18, 18)
images = shared(numpy.arange(numpy.prod(shape),
dtype='float32').reshape(shape))
neib_shape = T.as_tensor_variable((3, 3))
f = function([], images2neibs(images,neib_shape))#, mode=mode_without_gpu)
f = function([], images2neibs(images, neib_shape))
for i in range(1000):
f()
def speed_neibs_wrap_centered():
shape = (100,40,18,18)
images = shared(numpy.arange(numpy.prod(shape), dtype='float32').reshape(shape))
neib_shape = T.as_tensor_variable((3,3))
from theano.sandbox.cuda.basic_ops import gpu_from_host
def speed_neibs_wrap_centered():
shape = (100, 40, 18, 18)
images = shared(numpy.arange(numpy.prod(shape),
dtype='float32').reshape(shape))
neib_shape = T.as_tensor_variable((3, 3))
f = function([], images2neibs(images,neib_shape,mode="wrap_centered"))#, mode=mode_without_gpu)
f = function([], images2neibs(images, neib_shape, mode="wrap_centered"))
for i in range(1000):
f()
def test_neibs_grad():
shape = (2,3,4,4)
images = shared(numpy.arange(numpy.prod(shape), dtype='float32').reshape(shape))
cost = T.sum(T.sqr(images2neibs(images, (2,2))), axis=[0,1])
grad = T.grad(cost, images)
f = theano.function([], [cost, grad], mode=mode_without_gpu)
got = f()
should_get = [numpy.asarray(290320.0, dtype=numpy.float32),
numpy.asarray([[[[ 0., 2., 4., 6.],
[ 8., 10., 12., 14.],
[ 16., 18., 20., 22.],
[ 24., 26., 28., 30.]],
[[ 32., 34., 36., 38.],
[ 40., 42., 44., 46.],
[ 48., 50., 52., 54.],
[ 56., 58., 60., 62.]],
[[ 64., 66., 68., 70.],
[ 72., 74., 76., 78.],
[ 80., 82., 84., 86.],
[ 88., 90., 92., 94.]]],
[[[ 96., 98., 100., 102.],
[ 104., 106., 108., 110.],
[ 112., 114., 116., 118.],
[ 120., 122., 124., 126.]],
[[ 128., 130., 132., 134.],
[ 136., 138., 140., 142.],
[ 144., 146., 148., 150.],
[ 152., 154., 156., 158.]],
[[ 160., 162., 164., 166.],
[ 168., 170., 172., 174.],
[ 176., 178., 180., 182.],
[ 184., 186., 188., 190.]]]], dtype=numpy.float32)]
assert numpy.allclose(got[0], should_get[0])
assert numpy.allclose(got[1], should_get[1])
def test_neibs_grad_verify_grad():
shape = (2,3,4,4)
# Disable the test as the grad is wrongly implemented
def tes_neibs_grad_verify_grad():
shape = (2, 3, 4, 4)
images = T.dtensor4()
images_val = numpy.arange(numpy.prod(shape), dtype='float32').reshape(shape)
images_val = numpy.arange(numpy.prod(shape),
dtype='float32').reshape(shape)
def fn(images):
return T.sum(T.sqr(images2neibs(images, (2,2))), axis=[0,1])
return T.sum(T.sqr(images2neibs(images, (2, 2))), axis=[0, 1])
unittest_tools.verify_grad(fn, [images_val], mode=mode_without_gpu)
if cuda.cuda_available:
unittest_tools.verify_grad(fn, [images_val], mode=mode_with_gpu)
def test_neibs_grad_verify_grad_warp_centered():
# It is not implemented for now. So test that we raise an error.
shape = (2,3,6,6)
shape = (2, 3, 6, 6)
images = T.dtensor4()
images_val = numpy.arange(numpy.prod(shape), dtype='float32').reshape(shape)
images_val = numpy.arange(numpy.prod(shape),
dtype='float32').reshape(shape)
def fn(images):
return T.sum(T.sqr(images2neibs(images, (3,3),mode='wrap_centered')), axis=[0,1])
return T.sum(T.sqr(images2neibs(images, (3, 3), mode='wrap_centered')),
axis=[0, 1])
try:
unittest_tools.verify_grad(fn, [images_val], mode=mode_without_gpu)
raise Exception("Expected an error")
......@@ -411,42 +399,52 @@ def test_neibs_grad_verify_grad_warp_centered():
except NotImplementedError:
pass
def test_neibs_ignore_border():
shape = (2,3,5,5)
shape = (2, 3, 5, 5)
images = T.dtensor4()
images_val = numpy.arange(numpy.prod(shape), dtype='float32').reshape(shape)
images_val = numpy.arange(numpy.prod(shape),
dtype='float32').reshape(shape)
def fn(images):
return T.sum(T.sqr(images2neibs(images, (2,2), mode='ignore_borders')), axis=[0,1])
return T.sum(T.sqr(images2neibs(images, (2, 2),
mode='ignore_borders')), axis=[0, 1])
# Disable the test as the grad is wrongly implemented
#unittest_tools.verify_grad(fn, [images_val], mode=mode_without_gpu)
unittest_tools.verify_grad(fn, [images_val], mode=mode_without_gpu)
# not implemented for gpu
# if cuda.cuda_available:
# unittest_tools.verify_grad(fn, [images_val], mode=mode_with_gpu)
def test_neibs_valid_with_inconsistent_borders():
shape = (2,3,5,5)
shape = (2, 3, 5, 5)
images = T.dtensor4()
images_val = numpy.arange(numpy.prod(shape), dtype='float32').reshape(shape)
images_val = numpy.arange(numpy.prod(shape),
dtype='float32').reshape(shape)
def fn(images):
return T.sum(T.sqr(images2neibs(images, (2,2), mode='valid')), axis=[0,1])
return T.sum(T.sqr(images2neibs(images, (2, 2), mode='valid')),
axis=[0, 1])
f = theano.function([images],
T.sqr(images2neibs(images, (2, 2), mode='valid')))
try:
unittest_tools.verify_grad(fn, [images_val], mode=mode_without_gpu)
assert False,"An error was expected"
except TypeError:
f(images_val)
assert False, "An error was expected"
except TypeError, e:
# This is expected if the assert is there
pass
def test_neibs2images_crash_on_grad():
# say we had images of size (2,3,20,20)
# then we extracted 2x2 neighbors on this, we get (2*3*10*10, 4)
# Disable the test as the grad is wrongly implemented
def tes_neibs2images_crash_on_grad():
# say we had images of size (2, 3, 20, 20)
# then we extracted 2x2 neighbors on this, we get (2 * 3 * 10 * 10, 4)
neibs = T.dmatrix()
neibs_val = numpy.random.rand(600,4)
to_images = T.sum(neibs2images(neibs, (2,2), (2,3,20,20)))
neibs_val = numpy.random.rand(600, 4)
to_images = T.sum(neibs2images(neibs, (2, 2), (2, 3, 20, 20)))
g = T.grad(to_images, neibs)
fn = theano.function([neibs], to_images, mode=mode_without_gpu)
print "Compiled"
......
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