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)