def setUp(self):
"""
Set up a test image and filter to re-use
"""
skip_if_no_gpu()
self.image = \
numpy.random.rand(16, 3, 3, 1).astype(theano.config.floatX)
self.image_tensor = tensor.tensor4()
self.filters_values = numpy.ones((2, 2, 16, 2, 2, 1, 16),
dtype=theano.config.floatX)
self.filters = sharedX(self.filters_values, name='filters')
self.local = Local(self.filters, (3, 3), 1)
class TestConv2DC01b(unittest.TestCase):
"""
Test for local receptive fields
"""
def setUp(self):
"""
Set up a test image and filter to re-use
"""
skip_if_no_gpu()
self.image = \
numpy.random.rand(16, 3, 3, 1).astype(theano.config.floatX)
self.image_tensor = tensor.tensor4()
self.filters_values = numpy.ones(
(2, 2, 16, 2, 2, 1, 16), dtype=theano.config.floatX
)
self.filters = sharedX(self.filters_values, name='filters')
self.local = Local(self.filters, (3, 3), 1)
def test_get_params(self):
"""
Check whether the local receptive field has stored the correct filters
"""
assert self.local.get_params() == [self.filters]
def test_lmul(self):
"""
Make sure the shape of the output is correct
"""
f = theano.function([self.image_tensor],
self.local.lmul(self.image_tensor))
assert f(self.image).shape == (16, 2, 2, 1)
def test_make_random_local(self):
"""
Create random local receptive fields and check whether they can be
applied and give a sensible output shape
"""
local = make_random_local(1, 16, ('c', 0, 1, 'b'), 1, (3, 3),
16, ('c', 0, 1, 'b'), (2, 2))
f = theano.function([self.image_tensor],
local.lmul(self.image_tensor))
assert f(self.image).shape == (16, 2, 2, 1)
class TestConv2DC01b(unittest.TestCase):
"""
Test for local receptive fields
"""
def setUp(self):
"""
Set up a test image and filter to re-use
"""
skip_if_no_gpu()
self.image = \
numpy.random.rand(16, 3, 3, 1).astype(theano.config.floatX)
self.image_tensor = tensor.tensor4()
self.filters_values = numpy.ones((2, 2, 16, 2, 2, 1, 16),
dtype=theano.config.floatX)
self.filters = sharedX(self.filters_values, name='filters')
self.local = Local(self.filters, (3, 3), 1)
def test_get_params(self):
"""
Check whether the local receptive field has stored the correct filters
"""
assert self.local.get_params() == [self.filters]
def test_lmul(self):
"""
Make sure the shape of the output is correct
"""
f = theano.function([self.image_tensor],
self.local.lmul(self.image_tensor))
assert f(self.image).shape == (16, 2, 2, 1)
def test_make_random_local(self):
"""
Create random local receptive fields and check whether they can be
applied and give a sensible output shape
"""
local = make_random_local(1, 16, ('c', 0, 1, 'b'), 1, (3, 3), 16,
('c', 0, 1, 'b'), (2, 2))
f = theano.function([self.image_tensor], local.lmul(self.image_tensor))
assert f(self.image).shape == (16, 2, 2, 1)
def setUp(self):
"""
Set up a test image and filter to re-use
"""
skip_if_no_gpu()
self.image = \
numpy.random.rand(16, 3, 3, 1).astype(theano.config.floatX)
self.image_tensor = tensor.tensor4()
self.filters_values = numpy.ones(
(2, 2, 16, 2, 2, 1, 16), dtype=theano.config.floatX
)
self.filters = sharedX(self.filters_values, name='filters')
self.local = Local(self.filters, (3, 3), 1)
