def setUp(self):
self.op = FilterActs(module_stride=self.module_stride, openmp=False)
self.op_omp = FilterActs(module_stride=self.module_stride, openmp=True)
self.s_filters_list = [
theano.shared(rand(fshape, self.dtype))
for fshape in self.fshape_list
]
self.s_images_list = [
theano.shared(rand(ishape, self.dtype))
for ishape in self.ishape_list
]
def setUp(self):
self.op = FilterActs(self.module_stride)
for i in range(self.nbTests):
self.s_images_list = [
theano.shared(rand(ishape, self.dtype))
for ishape in self.ishape_list
]
self.s_filters_list = [
theano.shared(rand(fshape, self.dtype))
for fshape in self.fshape_list
]
def Toncv(image,filters,module_stride=1):
op = FilterActs(module_stride)
return op(image,filters)
def test_op_eq(self):
assert FilterActs(1) == FilterActs(1)
assert not (FilterActs(1) != FilterActs(1))
assert (FilterActs(2) != FilterActs(1))
assert FilterActs(1) != None
def testMainOpSpeed(self):
def do_time(output, mode=theano.Mode(linker='c')):
f = theano.function([], output, mode=mode)
t0 = time.time()
[f() for i in range(self.n_calls)]
t1 = time.time()
return t1 - t0
for i in range(self.nbTests):
print "image shape", self.ishape_list[i]
print "filter shape", self.fshape_list[i]
# Generate theano functions to run the op in python and in C
output = self.op(self.s_images_list[i], self.s_filters_list[i])
output_omp = self.op_omp(self.s_images_list[i],
self.s_filters_list[i])
output_fcols = FilterActs(
module_stride=self.module_stride,
openmp=False,
fcols=self.fshape_list[i][-1])(self.s_images_list[i],
self.s_filters_list[i])
output_fcols_omp = FilterActs(
module_stride=self.module_stride,
openmp=True,
fcols=self.fshape_list[i][-1])(self.s_images_list[i],
self.s_filters_list[i])
output_frows_fcols = FilterActs(
module_stride=self.module_stride,
openmp=False,
fcols=self.fshape_list[i][-1],
frows=self.fshape_list[i][-2])(self.s_images_list[i],
self.s_filters_list[i])
output_frows_fcols_omp = FilterActs(
module_stride=self.module_stride,
openmp=True,
fcols=self.fshape_list[i][-1],
frows=self.fshape_list[i][-2])(self.s_images_list[i],
self.s_filters_list[i])
# Run the OP in python
py_t = do_time(output, mode=theano.Mode(linker='py'))
print "py", py_t
# Run the OP in C
c_t = do_time(output, mode=theano.Mode(linker='c|py'))
print "c|py", c_t, "speed up", py_t / c_t
# Run the OP in C with fcols
c_t_fcols = do_time(output_fcols)
print "c fcols", c_t_fcols, "speed up", py_t / c_t_fcols
# Run the OP in C with fcols, frows
c_t_frows_fcols = do_time(output_frows_fcols)
print "c frows_fcols", c_t_frows_fcols, "speed up", py_t / c_t_frows_fcols
# Run the Op in C with openmp
if theano.config.openmp:
c_omp_t = do_time(output_omp)
print "omp c", c_omp_t, "speed up python", py_t / c_omp_t, "speed up c", c_t / c_omp_t
c_omp_fcols_t = do_time(output_fcols_omp)
print "omp c fcols", c_omp_fcols_t, "speed up python", py_t / c_omp_fcols_t, "speed up c fcols", c_t_fcols / c_omp_fcols_t
c_omp_frows_fcols_t = do_time(output_frows_fcols_omp)
print "omp c fcols", c_omp_frows_fcols_t, "speed up python", py_t / c_omp_frows_fcols_t, "speed up c frows_fcols", c_t_frows_fcols / c_omp_frows_fcols_t